PART III a): German Credit Score Classification Model BIAS & FAIRNESS (Age as protected variable).

By: Krishna J

Importing necessary libraries

In [1]:
import pandas as pd
import numpy as np
import seaborn               as sns
import matplotlib.pyplot     as plt
from sklearn.model_selection import train_test_split
#from sklearn.ensemble        import RandomForestClassifier
#from sklearn.linear_model    import LogisticRegression
from sklearn.preprocessing   import MinMaxScaler, StandardScaler
from sklearn.base            import TransformerMixin
from sklearn.pipeline        import Pipeline, FeatureUnion
from typing                  import List, Union, Dict
# Warnings will be used to silence various model warnings for tidier output
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline 
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
np.random.seed(0)

Importing source dataset

In [2]:
German_df = pd.read_csv('C:/Users/krish/Downloads/German-reduced.csv')

print(German_df.shape)
print (German_df.columns)
(1000, 24)
Index(['Gender', 'Age', 'Marital_Status', 'NumMonths', 'Savings_<500',
       'Savings_none', 'Dependents', 'Property_rent',
       'Job_management/self-emp/officer/highly qualif emp',
       'Debtors_guarantor', 'Purpose_CarNew', 'Purpose_furniture/equip',
       'CreditHistory_none/paid', 'Purpose_CarUsed', 'CreditAmount',
       'Collateral_real estate', 'Debtors_none',
       'Job_unemp/unskilled-non resident', 'Purpose_others',
       'CreditHistory_other', 'PayBackPercent', 'Collateral_unknown/none',
       'Purpose_education', 'CreditStatus'],
      dtype='object')
In [3]:
German_df.head()
Out[3]:
Gender Age Marital_Status NumMonths Savings_<500 Savings_none Dependents Property_rent Job_management/self-emp/officer/highly qualif emp Debtors_guarantor ... CreditAmount Collateral_real estate Debtors_none Job_unemp/unskilled-non resident Purpose_others CreditHistory_other PayBackPercent Collateral_unknown/none Purpose_education CreditStatus
0 1 1 1 6 0 1 1 0 0 0 ... 0.050567 1 1 0 0 1 4 0 0 1
1 0 0 0 48 1 0 1 0 0 0 ... 0.313690 1 1 0 0 0 2 0 0 0
2 1 1 1 12 1 0 2 0 0 0 ... 0.101574 1 1 0 0 1 2 0 1 1
3 1 1 1 42 1 0 2 0 0 1 ... 0.419941 0 0 0 0 0 2 0 0 1
4 1 1 1 24 1 0 2 0 0 0 ... 0.254209 0 1 0 0 0 3 1 0 0

5 rows × 24 columns

In [4]:
#feature_list = ['Gender','Age','Marital_Status','NumMonths','Savings_<500','Savings_none','Dependents','Property_rent','Job_management/self-emp/officer/highly qualif emp','Debtors_guarantor','Purpose_CarNew',                           'Purpose_furniture/equip','CreditHistory_none/paid','Purpose_CarUsed','CreditAmount','CreditStatus']
feature_list=['Gender','Age','Marital_Status','NumMonths','Savings_<500','Savings_none','Dependents','Property_rent',
                           'Job_management/self-emp/officer/highly qualif emp','Debtors_guarantor','Purpose_CarNew',
                           'Purpose_furniture/equip','CreditHistory_none/paid','Purpose_CarUsed','CreditAmount',
                           'Collateral_real estate','Debtors_none','Job_unemp/unskilled-non resident','Purpose_others',             
                            'CreditHistory_other','PayBackPercent','Collateral_unknown/none','Purpose_education', 'CreditStatus']
In [5]:
X = German_df.iloc[:, :-1]
y = German_df['CreditStatus']
X.head()
y.head()
Out[5]:
Gender Age Marital_Status NumMonths Savings_<500 Savings_none Dependents Property_rent Job_management/self-emp/officer/highly qualif emp Debtors_guarantor ... Purpose_CarUsed CreditAmount Collateral_real estate Debtors_none Job_unemp/unskilled-non resident Purpose_others CreditHistory_other PayBackPercent Collateral_unknown/none Purpose_education
0 1 1 1 6 0 1 1 0 0 0 ... 0 0.050567 1 1 0 0 1 4 0 0
1 0 0 0 48 1 0 1 0 0 0 ... 0 0.313690 1 1 0 0 0 2 0 0
2 1 1 1 12 1 0 2 0 0 0 ... 0 0.101574 1 1 0 0 1 2 0 1
3 1 1 1 42 1 0 2 0 0 1 ... 0 0.419941 0 0 0 0 0 2 0 0
4 1 1 1 24 1 0 2 0 0 0 ... 0 0.254209 0 1 0 0 0 3 1 0

5 rows × 23 columns

Out[5]:
0    1
1    0
2    1
3    1
4    0
Name: CreditStatus, dtype: int64

from imblearn.over_sampling import ADASYN from collections import Counter

ada = ADASYN(random_state=40) print('Original dataset shape {}'.format(Counter(y))) X_res, y_res = ada.fit_resample(X,y) print('Resampled dataset shape {}'.format(Counter(y_res)))

German_df=X = pd.DataFrame(np.column_stack((X_res, y_res)))

In [6]:
German_df.head()
Out[6]:
Gender Age Marital_Status NumMonths Savings_<500 Savings_none Dependents Property_rent Job_management/self-emp/officer/highly qualif emp Debtors_guarantor ... CreditAmount Collateral_real estate Debtors_none Job_unemp/unskilled-non resident Purpose_others CreditHistory_other PayBackPercent Collateral_unknown/none Purpose_education CreditStatus
0 1 1 1 6 0 1 1 0 0 0 ... 0.050567 1 1 0 0 1 4 0 0 1
1 0 0 0 48 1 0 1 0 0 0 ... 0.313690 1 1 0 0 0 2 0 0 0
2 1 1 1 12 1 0 2 0 0 0 ... 0.101574 1 1 0 0 1 2 0 1 1
3 1 1 1 42 1 0 2 0 0 1 ... 0.419941 0 0 0 0 0 2 0 0 1
4 1 1 1 24 1 0 2 0 0 0 ... 0.254209 0 1 0 0 0 3 1 0 0

5 rows × 24 columns

German_df.columns=feature_list German_df.head()

Metrics to calculate model fairness necessary libraries

In [7]:
from aif360.datasets import GermanDataset
from aif360.metrics import BinaryLabelDatasetMetric

def fair_metrics(fname, dataset, pred, pred_is_dataset=False):
    filename = fname
    if pred_is_dataset:
        dataset_pred = pred
    else:
        dataset_pred = dataset.copy()
        dataset_pred.labels = pred

    cols = ['Accuracy', 'F1', 'DI','SPD', 'EOD', 'AOD', 'ERD', 'CNT', 'TI']
    obj_fairness = [[1,1,1,0,0,0,0,1,0]]

    fair_metrics = pd.DataFrame(data=obj_fairness, index=['objective'], columns=cols)

    for attr in dataset_pred.protected_attribute_names:
        idx = dataset_pred.protected_attribute_names.index(attr)
        privileged_groups =  [{attr:dataset_pred.privileged_protected_attributes[idx][0]}]
        unprivileged_groups = [{attr:dataset_pred.unprivileged_protected_attributes[idx][0]}]

        classified_metric = ClassificationMetric(dataset,
                                                     dataset_pred,
                                                     unprivileged_groups=unprivileged_groups,
                                                     privileged_groups=privileged_groups)

        metric_pred = BinaryLabelDatasetMetric(dataset_pred,
                                                     unprivileged_groups=unprivileged_groups,
                                                     privileged_groups=privileged_groups)

        distortion_metric = SampleDistortionMetric(dataset,
                                                     dataset_pred,
                                                     unprivileged_groups=unprivileged_groups,
                                                     privileged_groups=privileged_groups)

        acc = classified_metric.accuracy()
        f1_sc = 2 * (classified_metric.precision() * classified_metric.recall()) / (classified_metric.precision() + classified_metric.recall())

        mt = [acc, f1_sc,
                        classified_metric.disparate_impact(),
                        classified_metric.mean_difference(),
                        classified_metric.equal_opportunity_difference(),
                        classified_metric.average_odds_difference(),
                        classified_metric.error_rate_difference(),
                        metric_pred.consistency(),
                        classified_metric.theil_index()
                    ]
        w_row = []
        print('Computing fairness of the model.')
        for i in mt:
            #print("%.8f"%i)
            w_row.append("%.8f"%i)
        with open(filename, 'a') as csvfile:
            csvwriter = csv.writer(csvfile)
            csvwriter.writerow(w_row)
        row = pd.DataFrame([mt],
                           columns  = cols,
                           index = [attr]
                          )
        fair_metrics = fair_metrics.append(row)
    fair_metrics = fair_metrics.replace([-np.inf, np.inf], 2)
    return fair_metrics

def get_fair_metrics_and_plot(fname, data, model, plot=False, model_aif=False):
    pred = model.predict(data).labels if model_aif else model.predict(data.features)
    fair = fair_metrics(fname, data, pred)
    if plot:
        pass

    return fair

def get_model_performance(X_test, y_true, y_pred, probs):
    accuracy = accuracy_score(y_true, y_pred)
    matrix = confusion_matrix(y_true, y_pred)
    f1 = f1_score(y_true, y_pred)
    return accuracy, matrix, f1

def plot_model_performance(model, X_test, y_true):
    y_pred = model.predict(X_test)
    probs = model.predict_proba(X_test)
    accuracy, matrix, f1 = get_model_performance(X_test, y_true, y_pred, probs)

Local file to load metric values

In [8]:
filename= 'C:/Users/krish/Downloads/filename_mainpjt_results_apr_19_age_na.csv'

Converting data to aif compatible format

Since we are dealing with binary label dataset we are using aif360 class BiaryLabelDataset here with target label as CreditStatus and protected attributes as age,gender,marital status. Refer part 11 for more details on protected attributes and privileged classes.

In [9]:
# Fairness metrics
from aif360.metrics import BinaryLabelDatasetMetric
from aif360.explainers import MetricTextExplainer
from aif360.metrics import ClassificationMetric
# Get DF into IBM format
from aif360 import datasets
#converting to aif dataset
aif_dataset = datasets.BinaryLabelDataset(favorable_label = 1, unfavorable_label = 0, df=German_df,
                                                      label_names=["CreditStatus"],
                                                     protected_attribute_names=["Age"],
                                              privileged_protected_attributes = [1])
In [10]:
#dataset_orig = GermanDataset(protected_attribute_names=['sex'],
#                            privileged_classes=[[1]],
#                            features_to_keep=['age', 'sex', 'employment', 'housing', 'savings', 'credit_amount', 'month', 'purpose'],
#                            custom_preprocessing=custom_preprocessing)

Splitting data to train and test sets

In [11]:
#privileged_groups = [{'Age':1},{' Gender': 1},{'Marital_Status':1}]
#unprivileged_groups = [{'Age':0},{'Gender': 0},{'Marital_Status':0}]
In [12]:
privileged_groups = [{'Age': 1}]
unprivileged_groups = [{'Age': 0}]
In [13]:
data_orig_train, data_orig_test = aif_dataset.split([0.7], shuffle=True)

X_train = data_orig_train.features
y_train = data_orig_train.labels.ravel()

X_test = data_orig_test.features
y_test = data_orig_test.labels.ravel()
In [14]:
X_train.shape
X_test.shape
Out[14]:
(700, 23)
Out[14]:
(300, 23)
In [15]:
data_orig_test.labels[:10].ravel()
Out[15]:
array([0., 0., 1., 1., 1., 0., 1., 1., 1., 1.])
In [16]:
data_orig_train.labels[:10].ravel()
Out[16]:
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 0.])
In [290]:
metric_orig_train = BinaryLabelDatasetMetric(data_orig_train, 
                                             unprivileged_groups=unprivileged_groups,
                                             privileged_groups=privileged_groups)
print("Difference in mean outcomes between unprivileged and privileged groups = %f" % metric_orig_train.mean_difference())
Difference in mean outcomes between unprivileged and privileged groups = -0.193645

Building ML model

Considering ensemble models for our study.

1. RANDOM FOREST CLASSIFIER MODEL

In [17]:
#Seting the Hyper Parameters
param_grid = {"max_depth": [3,5,7, 10,None],
              "n_estimators":[3,5,10,25,50,150],
              "max_features": [4,7,15,20]}
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
#Creating the classifier
rf_model = RandomForestClassifier(random_state=40)
grid_search = GridSearchCV(rf_model, param_grid=param_grid, cv=5, scoring='recall', verbose=0)
model_rf = grid_search
In [18]:
mdl_rf = model_rf.fit(data_orig_train.features, data_orig_train.labels.ravel())
In [19]:
from sklearn.metrics import confusion_matrix
conf_mat_rf = confusion_matrix(data_orig_test.labels.ravel(), model_rf.predict(data_orig_test.features))
conf_mat_rf
from sklearn.metrics import accuracy_score
print(accuracy_score(data_orig_test.labels.ravel(), model_rf.predict(data_orig_test.features)))
Out[19]:
array([[  3,  87],
       [  0, 210]], dtype=int64)
0.71
In [20]:
unique, counts = np.unique(data_orig_test.labels.ravel(), return_counts=True)
dict(zip(unique, counts))
Out[20]:
{0.0: 90, 1.0: 210}

1.a. Feature importance of model

In [21]:
importances = model_rf.best_estimator_.feature_importances_
indices = np.argsort(importances)
features = data_orig_train.feature_names
#https://stackoverflow.com/questions/48377296/get-feature-importance-from-gridsearchcv
In [22]:
importances
Out[22]:
array([0.03339163, 0.06129979, 0.03458668, 0.1549452 , 0.08652746,
       0.06627319, 0.00340989, 0.0251218 , 0.01348329, 0.00826302,
       0.06000396, 0.00363823, 0.07900086, 0.0084767 , 0.17899456,
       0.01492402, 0.0018684 , 0.01691877, 0.00576587, 0.09671483,
       0.02132972, 0.0071254 , 0.01793675])
In [23]:
importances[indices]
Out[23]:
array([0.0018684 , 0.00340989, 0.00363823, 0.00576587, 0.0071254 ,
       0.00826302, 0.0084767 , 0.01348329, 0.01492402, 0.01691877,
       0.01793675, 0.02132972, 0.0251218 , 0.03339163, 0.03458668,
       0.06000396, 0.06129979, 0.06627319, 0.07900086, 0.08652746,
       0.09671483, 0.1549452 , 0.17899456])
In [24]:
features
Out[24]:
['Gender',
 'Age',
 'Marital_Status',
 'NumMonths',
 'Savings_<500',
 'Savings_none',
 'Dependents',
 'Property_rent',
 'Job_management/self-emp/officer/highly qualif emp',
 'Debtors_guarantor',
 'Purpose_CarNew',
 'Purpose_furniture/equip',
 'CreditHistory_none/paid',
 'Purpose_CarUsed',
 'CreditAmount',
 'Collateral_real estate',
 'Debtors_none',
 'Job_unemp/unskilled-non resident',
 'Purpose_others',
 'CreditHistory_other',
 'PayBackPercent',
 'Collateral_unknown/none',
 'Purpose_education']
In [25]:
plt.figure(figsize=(20,30))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), [features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()
Out[25]:
<Figure size 1440x2160 with 0 Axes>
Out[25]:
Text(0.5, 1.0, 'Feature Importances')
Out[25]:
<BarContainer object of 23 artists>
Out[25]:
([<matplotlib.axis.YTick at 0x293187f2788>,
  <matplotlib.axis.YTick at 0x2931165a848>,
  <matplotlib.axis.YTick at 0x293188822c8>,
  <matplotlib.axis.YTick at 0x2931a93b548>,
  <matplotlib.axis.YTick at 0x2931a93dd08>,
  <matplotlib.axis.YTick at 0x2931a93f748>,
  <matplotlib.axis.YTick at 0x2931a943088>,
  <matplotlib.axis.YTick at 0x2931a9437c8>,
  <matplotlib.axis.YTick at 0x2931a9480c8>,
  <matplotlib.axis.YTick at 0x2931a948808>,
  <matplotlib.axis.YTick at 0x2931a94d0c8>,
  <matplotlib.axis.YTick at 0x2931a94d988>,
  <matplotlib.axis.YTick at 0x2931a948448>,
  <matplotlib.axis.YTick at 0x2931a93fbc8>,
  <matplotlib.axis.YTick at 0x2931a951548>,
  <matplotlib.axis.YTick at 0x2931a953188>,
  <matplotlib.axis.YTick at 0x2931a9536c8>,
  <matplotlib.axis.YTick at 0x2931a958288>,
  <matplotlib.axis.YTick at 0x2931a958c08>,
  <matplotlib.axis.YTick at 0x2931a95c648>,
  <matplotlib.axis.YTick at 0x2931a95f108>,
  <matplotlib.axis.YTick at 0x2931a95fa88>,
  <matplotlib.axis.YTick at 0x2931a958d08>],
 <a list of 23 Text yticklabel objects>)
Out[25]:
Text(0.5, 0, 'Relative Importance')

1.b. Model Explainability/interpretability

1.b.1 Using SHAP (SHapley Additive exPlanations)

In [26]:
import shap

Test data interpretation

In [27]:
rf_explainer = shap.KernelExplainer(model_rf.predict, data_orig_test.features)
rf_shap_values = rf_explainer.shap_values(data_orig_test.features,nsamples=50)
#https://towardsdatascience.com/explain-any-models-with-the-shap-values-use-the-kernelexplainer-79de9464897a
Using 300 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

In [28]:
rf_shap_values
Out[28]:
array([[ 0.        ,  0.00208812,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.02612348, ...,  0.        ,
        -0.00539599,  0.        ],
       [ 0.00111976,  0.00137532,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       ...,
       [ 0.00029608,  0.00013956,  0.00010948, ...,  0.        ,
         0.        ,  0.00153509],
       [ 0.        ,  0.00067686,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.00265505,  0.        , ...,  0.        ,
         0.        ,  0.        ]])
In [29]:
rf_explainer.expected_value
Out[29]:
0.9900000000000001
In [30]:
y_test_predict=model_rf.predict(data_orig_test.features)
y_test_predict[:12]
data_orig_test.labels[:12].ravel()
data_orig_test.features[:2,:]
Out[30]:
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
Out[30]:
array([0., 0., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1.])
Out[30]:
array([[1.00000000e+00, 1.00000000e+00, 0.00000000e+00, 2.40000000e+01,
        1.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,
        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
        0.00000000e+00, 0.00000000e+00, 4.25883130e-02, 1.00000000e+00,
        1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
        4.00000000e+00, 0.00000000e+00, 0.00000000e+00],
       [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 6.00000000e+01,
        1.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,
        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
        0.00000000e+00, 0.00000000e+00, 3.62385826e-01, 0.00000000e+00,
        1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
        3.00000000e+00, 1.00000000e+00, 0.00000000e+00]])
In [31]:
y_test_predict.mean()
Out[31]:
0.99

The explainer expected value is the average model predicted value on input data. Shapely helps to understand how individual features impact the output of each individual instance. The shapely values are model predicted values which may not coincide with actual y test values due to prediction error.

link=”logit” argument converts the logit values to probability

In [32]:
shap.initjs()
shap.force_plot(rf_explainer.expected_value,rf_shap_values[0],data_orig_test.features[0],data_orig_test.feature_names,link='logit')
#https://github.com/slundberg/shap
#https://github.com/slundberg/shap/issues/279
#https://github.com/slundberg/shap/issues/977
shap.initjs()
shap.force_plot(rf_explainer.expected_value,rf_shap_values[0],data_orig_test.features[0],data_orig_test.feature_names)
Out[32]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
Out[32]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.

Features in blue pushes the base value towards lowest values and features in red moves base levels towards higher values.

Shapley values calculate the importance of a feature by comparing what a model predicts with and without the feature. However, since the order in which a model sees features can affect its predictions, this is done in every possible order, so that the features are fairly compared.

The SHAP plot shows features that contribute to pushing the output from the base value (average model output) to the actual predicted value.

In [33]:
shap.initjs()
shap.force_plot(rf_explainer.expected_value,rf_shap_values[1], data_orig_test.features[1],data_orig_test.feature_names,link='logit')
shap.initjs()
shap.force_plot(rf_explainer.expected_value,rf_shap_values[1], data_orig_test.features[1],data_orig_test.feature_names)
Out[33]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
Out[33]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [34]:
data_orig_test.feature_names
Out[34]:
['Gender',
 'Age',
 'Marital_Status',
 'NumMonths',
 'Savings_<500',
 'Savings_none',
 'Dependents',
 'Property_rent',
 'Job_management/self-emp/officer/highly qualif emp',
 'Debtors_guarantor',
 'Purpose_CarNew',
 'Purpose_furniture/equip',
 'CreditHistory_none/paid',
 'Purpose_CarUsed',
 'CreditAmount',
 'Collateral_real estate',
 'Debtors_none',
 'Job_unemp/unskilled-non resident',
 'Purpose_others',
 'CreditHistory_other',
 'PayBackPercent',
 'Collateral_unknown/none',
 'Purpose_education']
In [35]:
shap.force_plot(rf_explainer.expected_value,
                rf_shap_values, data_orig_test.features[:,:],feature_names = data_orig_test.feature_names)
Out[35]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [36]:
p = shap.summary_plot(rf_shap_values, data_orig_test.features, feature_names=data_orig_test.feature_names,plot_type="bar") 
display(p)
None

Variables with higher impact are displayed at the credit history, credit amount,num of months.

In [37]:
shap.decision_plot(rf_explainer.expected_value, rf_shap_values,feature_names=data_orig_test.feature_names)
  • The x-axis represents the model's output. In this case, the units are log odds.
  • The plot is centered on the x-axis at explainer.expected_value.
  • All SHAP values are relative to the model's expected value like a linear model's effects are relative to the intercept.
  • The y-axis lists the model's features.
  • By default, the features are ordered by descending importance. The importance is calculated over the observations plotted. This is usually different than the importance ordering for the entire dataset.
  • In addition to feature importance ordering, the decision plot also supports hierarchical cluster feature ordering and user-defined feature ordering.
  • Each observation's prediction is represented by a colored line. At the top of the plot, each line strikes the x-axis at its corresponding observation's predicted value. This value determines the color of the line on a spectrum.
  • Moving from the bottom of the plot to the top, SHAP values for each feature are added to the model's base value. This shows how each feature contributes to the overall prediction.
  • At the bottom of the plot, the observations converge at explainer.expected_value https://slundberg.github.io/shap/notebooks/plots/decision_plot.html

Like the force plot, the decision plot supports link='logit' to transform log odds to probabilities.

In [38]:
shap.decision_plot(rf_explainer.expected_value, rf_shap_values,feature_names=data_orig_test.feature_names,link='logit')
In [39]:
shap.plots._waterfall.waterfall_legacy(rf_explainer.expected_value, rf_shap_values[0],feature_names=data_orig_test.feature_names)

For first instace of input,out of all the displayed variables, CreditHistory with value other is playing major role is pushing the target variable outcome towards predicting 1.

Interpretation of graph: https://shap.readthedocs.io/en/latest/example_notebooks/overviews/An%20introduction%20to%20explainable%20AI%20with%20Shapley%20values.html

f(x)- model output impacted by features; E(f(x))- expected output.

One the fundemental properties of Shapley values is that they always sum up to the difference between the game outcome when all players are present and the game outcome when no players are present. For machine learning models this means that SHAP values of all the input features will always sum up to the difference between baseline (expected) model output and the current model output for the prediction being explained.

Shapley values calculate the importance of a feature by comparing what a model predicts with and without the feature. However, since the order in which a model sees features can affect its predictions, this is done in every possible order, so that the features are fairly compared. https://medium.com/@gabrieltseng/interpreting-complex-models-with-shap-values-1c187db6ec83

In [40]:
shap.plots._waterfall.waterfall_legacy(rf_explainer.expected_value, rf_shap_values[1],feature_names=data_orig_test.feature_names)

For second instace of input,out of all the displayed variables, credit amount is playing major role is pushing the target variable outcome towards predicting 0.

1.b.2 Using ELI5

In [41]:
#!pip install eli5
import eli5
In [42]:
from eli5.sklearn import PermutationImportance
In [43]:
perm_rf = PermutationImportance(mdl_rf).fit(data_orig_test.features, data_orig_test.labels.ravel())

Feature Importance

In [44]:
perm_imp_1=eli5.show_weights(perm_rf,feature_names = data_orig_test.feature_names)
perm_imp_1
plt.show()
Out[44]:
Weight Feature
0.0038 ± 0.0038 Savings_none
0.0038 ± 0.0071 Purpose_CarNew
0.0029 ± 0.0047 CreditHistory_none/paid
0.0029 ± 0.0047 Savings_<500
0.0019 ± 0.0047 CreditAmount
0.0019 ± 0.0047 Age
0.0019 ± 0.0047 Gender
0.0010 ± 0.0038 Marital_Status
0.0010 ± 0.0038 Property_rent
0.0010 ± 0.0038 NumMonths
0 ± 0.0000 Collateral_real estate
0 ± 0.0000 Collateral_unknown/none
0 ± 0.0000 Debtors_guarantor
0 ± 0.0000 PayBackPercent
0 ± 0.0000 Job_management/self-emp/officer/highly qualif emp
0 ± 0.0000 Dependents
0 ± 0.0000 Purpose_furniture/equip
0 ± 0.0000 Purpose_CarUsed
0 ± 0.0000 Purpose_education
0 ± 0.0000 Job_unemp/unskilled-non resident
… 3 more …
  • eli5 provides a way to compute feature importances for any black-box estimator by measuring how score decreases when a feature is not available; the method is also known as “permutation importance” or “Mean Decrease Accuracy (MDA)”.
  • The first number in each row shows how much model performance decreased with a random shuffling (in this case, using "accuracy" as the performance metric).

  • Like most things in data science, there is some randomness to the exact performance change from a shuffling a column. We measure the amount of randomness in our permutation importance calculation by repeating the process with multiple shuffles. The number after the ± measures how performance varied from one-reshuffling to the next.

  • You'll occasionally see negative values for permutation importances. In those cases, the predictions on the shuffled (or noisy) data happened to be more accurate than the real data. This happens when the feature didn't matter (should have had an importance close to 0), but random chance caused the predictions on shuffled data to be more accurate. This is more common with small datasets, like the one in this example, because there is more room for luck/chance.

https://www.kaggle.com/dansbecker/permutation-importance

1.c. Measuring fairness

Of Baseline model

In [45]:
import pandas as pd
import csv
import os
import numpy as np
import sys
from aif360.metrics import *
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, roc_curve, auc
plot_model_performance(mdl_rf, X_test, y_test)
In [46]:
fair_rf = get_fair_metrics_and_plot(filename, data_orig_test, mdl_rf)
fair_rf
Computing fairness of the model.
Out[46]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.00 1.000000 1.000000 0.000000 0.0 0.000000 0.000000 1.000000 0.000000
Age 0.71 0.828402 0.983817 -0.016058 0.0 -0.025475 -0.004983 0.987333 0.057005
In [47]:
type(data_orig_train)
Out[47]:
aif360.datasets.binary_label_dataset.BinaryLabelDataset

PRE PROCESSING

In [48]:
### Reweighing
from aif360.algorithms.preprocessing import Reweighing

RW_rf = Reweighing(unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)

data_transf_train_rf_rw = RW_rf.fit_transform(data_orig_train)
#train and save model
rf_transf_rw = model_rf.fit(data_transf_train_rf_rw.features,
                     data_transf_train_rf_rw.labels.ravel())

data_transf_test_rf_rw = RW_rf.transform(data_orig_test)
fair_rf_rw = get_fair_metrics_and_plot(filename, data_transf_test_rf_rw, rf_transf_rw, plot=False)
WARNING:root:No module named 'numba.decorators': LFR will be unavailable. To install, run:
pip install 'aif360[LFR]'
Computing fairness of the model.
In [294]:
metric_transf_train = BinaryLabelDatasetMetric(data_transf_train_rf_rw, 
                                               unprivileged_groups=unprivileged_groups,
                                               privileged_groups=privileged_groups)
print("Difference in mean outcomes between unprivileged and privileged groups = %f" % metric_transf_train.mean_difference())
Difference in mean outcomes between unprivileged and privileged groups = -0.000000
In [49]:
fair_rf_rw
Out[49]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.0 0.000000 0.000000 1.000000 0.000000
Age 0.690051 0.814465 0.994353 -0.005597 0.0 -0.025475 -0.159849 0.987333 0.057005
In [50]:
from aif360.algorithms.preprocessing import DisparateImpactRemover

DIR_rf = DisparateImpactRemover()
data_transf_train_rf_dir = DIR_rf.fit_transform(data_orig_train)

# Train and save the model
rf_transf_dir = model_rf.fit(data_transf_train_rf_dir.features,data_transf_train_rf_dir.labels.ravel())
In [51]:
fair_dir_rf_dir = get_fair_metrics_and_plot(filename,data_orig_test, rf_transf_dir, plot=False)
fair_dir_rf_dir
Computing fairness of the model.
Out[51]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000 0.0000
Age 0.706667 0.826087 0.959821 -0.039867 -0.034483 -0.042716 0.018826 0.984 0.0604

INPROCESSING

In [52]:
#!pip install --user --upgrade tensorflow==1.15.0
#2.2.0
#!pip uninstall tensorflow
In [53]:
#!pip install "tensorflow==1.15"
#!pip install --upgrade tensorflow-hub
In [54]:
#%tensorflow_version 1.15
import tensorflow  as tf
#from tensorflow.compat.v1 import variable_scope
print('Using TensorFlow version', tf.__version__)
Using TensorFlow version 1.15.0
In [55]:
#sess = tf.compat.v1.Session()
#import tensorflow as tf

sess = tf.compat.v1.Session()
In [300]:
#import tensorflow as tf
#sess = tf.Session()
tf.compat.v1.reset_default_graph()
In [301]:
from aif360.algorithms.inprocessing.adversarial_debiasing import AdversarialDebiasing
#with tf.variable_scope('debiased_classifier',reuse=tf.AUTO_REUSE):
with tf.compat.v1.Session() as sess:
    with tf.variable_scope('scope1',reuse=tf.AUTO_REUSE) as scope:
        debiased_model_rf_ad = AdversarialDebiasing(privileged_groups = privileged_groups,
                          unprivileged_groups = unprivileged_groups,
                          scope_name=scope,
                          num_epochs=10,
                          debias=True,
                          sess=sess)
#train and save the model
        debiased_model_rf_ad.fit(data_orig_train)
        fair_rf_ad = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_rf_ad, plot=False, model_aif=True)
epoch 0; iter: 0; batch classifier loss: 0.757019; batch adversarial loss: 0.717246
epoch 1; iter: 0; batch classifier loss: 0.912987; batch adversarial loss: 0.708956
epoch 2; iter: 0; batch classifier loss: 0.798356; batch adversarial loss: 0.703540
epoch 3; iter: 0; batch classifier loss: 0.796908; batch adversarial loss: 0.694601
epoch 4; iter: 0; batch classifier loss: 0.673781; batch adversarial loss: 0.698750
epoch 5; iter: 0; batch classifier loss: 0.671456; batch adversarial loss: 0.693009
epoch 6; iter: 0; batch classifier loss: 0.651770; batch adversarial loss: 0.691272
epoch 7; iter: 0; batch classifier loss: 0.598433; batch adversarial loss: 0.685915
epoch 8; iter: 0; batch classifier loss: 0.766303; batch adversarial loss: 0.678323
epoch 9; iter: 0; batch classifier loss: 0.604197; batch adversarial loss: 0.678681
Out[301]:
<aif360.algorithms.inprocessing.adversarial_debiasing.AdversarialDebiasing at 0x2933a0bba08>
Computing fairness of the model.
In [58]:
fair_rf_ad
Out[58]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.0 1.000000 1.0 0.0 0.0 0.0 0.000000 1 0.00000
Age 0.7 0.823529 1.0 0.0 0.0 0.0 0.011074 [1.0] 0.05755
In [59]:
from aif360.algorithms.inprocessing import PrejudiceRemover
debiased_model_pr_rf = PrejudiceRemover()

# Train and save the model
debiased_model_pr_rf.fit(data_orig_train)

fair_rf_pr = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_pr_rf, plot=False, model_aif=True)
fair_rf_pr
Out[59]:
<aif360.algorithms.inprocessing.prejudice_remover.PrejudiceRemover at 0x2931eb7df08>
Computing fairness of the model.
Out[59]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.00000 0.000000 0.00000 0.000000 1 0.000000
Age 0.696667 0.809224 0.719665 -0.25969 -0.346161 -0.20405 0.228682 [0.894] 0.113402
#
In [60]:
y_pred = debiased_model_pr_rf.predict(data_orig_test)


data_orig_test_pred = data_orig_test.copy(deepcopy=True)
In [61]:
# Prediction with the original RandomForest model
scores = np.zeros_like(data_orig_test.labels)
scores = mdl_rf.predict_proba(data_orig_test.features)[:,1].reshape(-1,1)
data_orig_test_pred.scores = scores

preds = np.zeros_like(data_orig_test.labels)
preds = mdl_rf.predict(data_orig_test.features).reshape(-1,1)
data_orig_test_pred.labels = preds

def format_probs(probs1):
    probs1 = np.array(probs1)
    probs0 = np.array(1-probs1)
    return np.concatenate((probs0, probs1), axis=1)

POST PROCESSING

In [62]:
from aif360.algorithms.postprocessing import EqOddsPostprocessing
EOPP_rf = EqOddsPostprocessing(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups,
                             seed=40)
EOPP_rf = EOPP_rf.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_rf_eopp = EOPP_rf.predict(data_orig_test_pred)
fair_rf_eo = fair_metrics(filename, data_orig_test, data_transf_test_pred_rf_eopp, pred_is_dataset=True)
Computing fairness of the model.
In [63]:
fair_rf_eo
Out[63]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.00000 1 0.000000
Age 0.703333 0.822355 0.978941 -0.020487 -0.017908 -0.021442 0.01495 [0.9566666666666667] 0.070261
In [64]:
from aif360.algorithms.postprocessing import CalibratedEqOddsPostprocessing
cost_constraint = "fnr"
CPP_rf = CalibratedEqOddsPostprocessing(privileged_groups = privileged_groups,
                                     unprivileged_groups = unprivileged_groups,
                                     cost_constraint=cost_constraint,
                                     seed=42)

CPP_rf = CPP_rf.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_rf_cpp = CPP_rf.predict(data_orig_test_pred)
fair_rf_ceo = fair_metrics(filename, data_orig_test, data_transf_test_pred_rf_cpp, pred_is_dataset=True)
Computing fairness of the model.
In [65]:
fair_rf_ceo
Out[65]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.0 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.7 0.822835 0.952381 -0.047619 -0.034483 -0.055703 0.011074 [0.9873333333333333] 0.060767
In [66]:
from aif360.algorithms.postprocessing import RejectOptionClassification
ROC_rf = RejectOptionClassification(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups)

ROC_rf = ROC_rf.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_rf_roc = ROC_rf.predict(data_orig_test_pred)
fair_rf_roc = fair_metrics(filename, data_orig_test, data_transf_test_pred_rf_roc, pred_is_dataset=True)
print('SUCCESS: completed 1 model.')
Computing fairness of the model.
SUCCESS: completed 1 model.
In [67]:
fair_rf_roc
Out[67]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.666667 0.736842 0.913127 -0.049834 -0.213374 0.058148 0.249169 [0.7826666666666663] 0.303058

2. XGBoost Classifier

In [68]:
from xgboost import XGBClassifier
estimator = XGBClassifier(seed=40)

parameters = {
    'max_depth': range (2, 10, 2),
    'n_estimators': range(60, 240, 40),
    'learning_rate': [0.1, 0.01, 0.05]
}
grid_search = GridSearchCV(
    estimator=estimator,
    param_grid=parameters,
    scoring = 'recall',
    
    cv = 5,
    verbose=0
)

model_xg=grid_search
In [69]:
mdl_xgb = model_xg.fit(data_orig_train.features, data_orig_train.labels.ravel())
In [70]:
conf_mat_xg = confusion_matrix(data_orig_test.labels.ravel(), model_xg.predict(data_orig_test.features))
conf_mat_xg
from sklearn.metrics import accuracy_score
print(accuracy_score(data_orig_test.labels.ravel(), model_xg.predict(data_orig_test.features)))
Out[70]:
array([[  2,  88],
       [  4, 206]], dtype=int64)
0.6933333333333334

2.a. Feature importance of model

In [71]:
importances_xg = model_xg.best_estimator_.feature_importances_
indices_xg = np.argsort(importances_xg)
features = data_orig_train.feature_names
#https://stackoverflow.com/questions/48377296/get-feature-importance-from-gridsearchcv
In [72]:
importances_xg
Out[72]:
array([0.06220475, 0.10700952, 0.        , 0.10817891, 0.15189382,
       0.083426  , 0.        , 0.        , 0.04445123, 0.        ,
       0.09484909, 0.        , 0.05951812, 0.        , 0.08711442,
       0.04637603, 0.        , 0.        , 0.        , 0.11213879,
       0.        , 0.        , 0.04283933], dtype=float32)
In [73]:
importances_xg[indices_xg]
Out[73]:
array([0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.04283933, 0.04445123, 0.04637603, 0.05951812,
       0.06220475, 0.083426  , 0.08711442, 0.09484909, 0.10700952,
       0.10817891, 0.11213879, 0.15189382], dtype=float32)
In [74]:
features
Out[74]:
['Gender',
 'Age',
 'Marital_Status',
 'NumMonths',
 'Savings_<500',
 'Savings_none',
 'Dependents',
 'Property_rent',
 'Job_management/self-emp/officer/highly qualif emp',
 'Debtors_guarantor',
 'Purpose_CarNew',
 'Purpose_furniture/equip',
 'CreditHistory_none/paid',
 'Purpose_CarUsed',
 'CreditAmount',
 'Collateral_real estate',
 'Debtors_none',
 'Job_unemp/unskilled-non resident',
 'Purpose_others',
 'CreditHistory_other',
 'PayBackPercent',
 'Collateral_unknown/none',
 'Purpose_education']
In [75]:
plt.figure(figsize=(20,30))
plt.title('Feature Importances')
plt.barh(range(len(indices_xg)), importances_xg[indices_xg], color='b', align='center')
plt.yticks(range(len(indices_xg)), [features[i] for i in indices_xg])
plt.xlabel('Relative Importance')
plt.show()
Out[75]:
<Figure size 1440x2160 with 0 Axes>
Out[75]:
Text(0.5, 1.0, 'Feature Importances')
Out[75]:
<BarContainer object of 23 artists>
Out[75]:
([<matplotlib.axis.YTick at 0x29324383648>,
  <matplotlib.axis.YTick at 0x2932439fc88>,
  <matplotlib.axis.YTick at 0x2932438d588>,
  <matplotlib.axis.YTick at 0x293243e30c8>,
  <matplotlib.axis.YTick at 0x293243e45c8>,
  <matplotlib.axis.YTick at 0x293243e4c08>,
  <matplotlib.axis.YTick at 0x293243e9308>,
  <matplotlib.axis.YTick at 0x293243e9988>,
  <matplotlib.axis.YTick at 0x293243ef288>,
  <matplotlib.axis.YTick at 0x293243e9f48>,
  <matplotlib.axis.YTick at 0x293243ef408>,
  <matplotlib.axis.YTick at 0x293243eff08>,
  <matplotlib.axis.YTick at 0x293243f35c8>,
  <matplotlib.axis.YTick at 0x293243f71c8>,
  <matplotlib.axis.YTick at 0x293243f7708>,
  <matplotlib.axis.YTick at 0x293243fb288>,
  <matplotlib.axis.YTick at 0x293243fb848>,
  <matplotlib.axis.YTick at 0x29324400108>,
  <matplotlib.axis.YTick at 0x29324400988>,
  <matplotlib.axis.YTick at 0x293243fb988>,
  <matplotlib.axis.YTick at 0x293243f3f88>,
  <matplotlib.axis.YTick at 0x293244042c8>,
  <matplotlib.axis.YTick at 0x29324404c48>],
 <a list of 23 Text yticklabel objects>)
Out[75]:
Text(0.5, 0, 'Relative Importance')

2.b. Model Explainability/interpretability

2.b.1 Using SHAP (SHapley Additive exPlanations)

In [76]:
import shap
xg_shap_values_t1 = shap.KernelExplainer(mdl_xgb.predict,data_orig_train.features)
WARNING:shap:Using 700 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

Test data interpretation

In [78]:
xgb_explainer = shap.KernelExplainer(mdl_xgb.predict, data_orig_test.features)
xgb_shap_values = xgb_explainer.shap_values(data_orig_test.features,nsamples=10)
#https://towardsdatascience.com/explain-any-models-with-the-shap-values-use-the-kernelexplainer-79de9464897a
WARNING:shap:Using 300 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

In [79]:
xgb_shap_values
Out[79]:
array([[0.        , 0.01166667, 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       ...,
       [0.        , 0.        , 0.        , ..., 0.        , 0.01      ,
        0.        ],
       [0.01166667, 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ]])
In [80]:
shap.initjs()
shap.force_plot(xgb_explainer.expected_value,xgb_shap_values[0,:], data_orig_test.features[0],data_orig_test.feature_names,link='logit')
#https://github.com/slundberg/shap
#https://github.com/slundberg/shap/issues/279
Out[80]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [81]:
shap.initjs()
shap.force_plot(xgb_explainer.expected_value,xgb_shap_values[1,:], data_orig_test.features[1],data_orig_test.feature_names,link='logit')
Out[81]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [82]:
shap.force_plot(xgb_explainer.expected_value,
                xgb_shap_values, data_orig_test.features[:,:],feature_names = data_orig_test.feature_names)
Out[82]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [83]:
p = shap.summary_plot(xgb_shap_values, data_orig_test.features, feature_names=data_orig_test.feature_names,plot_type="bar") 
display(p)
None

The variables with higher impact are the ones in the top age,gender,marital status.

In [84]:
shap.plots._waterfall.waterfall_legacy(xgb_explainer.expected_value, xgb_shap_values[0,:],feature_names=data_orig_test.feature_names)

Here credit history other and age are moving target outcome towards right i.e., 1.

Interpretation of graph: https://shap.readthedocs.io/en/latest/example_notebooks/overviews/An%20introduction%20to%20explainable%20AI%20with%20Shapley%20values.html

f(x)- model output impacted by features; E(f(x))- expected output.

One the fundemental properties of Shapley values is that they always sum up to the difference between the game outcome when all players are present and the game outcome when no players are present. For machine learning models this means that SHAP values of all the input features will always sum up to the difference between baseline (expected) model output and the current model output for the prediction being explained.

In [85]:
shap.plots._waterfall.waterfall_legacy(xgb_explainer.expected_value, xgb_shap_values[1],feature_names=data_orig_test.feature_names)

Here Credit Amount is moving the target result towards zero.

2.b.2 Using ELI5

In [86]:
#!pip install eli5
import eli5
from eli5.sklearn import PermutationImportance
In [87]:
perm_xgb = PermutationImportance(mdl_xgb).fit(data_orig_test.features, data_orig_test.labels.ravel())

Feature Importance

In [88]:
perm_imp_2=eli5.show_weights(perm_xgb,feature_names = data_orig_test.feature_names)
perm_imp_2
plt.show()
Out[88]:
Weight Feature
0.0038 ± 0.0038 Gender
0.0029 ± 0.0097 Purpose_CarNew
0 ± 0.0000 Savings_none
0 ± 0.0000 Dependents
0 ± 0.0000 Property_rent
0 ± 0.0000 Job_management/self-emp/officer/highly qualif emp
0 ± 0.0000 Debtors_guarantor
0 ± 0.0000 CreditHistory_none/paid
0 ± 0.0000 Purpose_CarUsed
0 ± 0.0000 Purpose_education
0 ± 0.0000 PayBackPercent
0 ± 0.0000 Purpose_furniture/equip
0 ± 0.0000 CreditHistory_other
0 ± 0.0000 Collateral_unknown/none
0 ± 0.0000 Purpose_others
0 ± 0.0000 Debtors_none
0 ± 0.0000 Collateral_real estate
0 ± 0.0000 Job_unemp/unskilled-non resident
0 ± 0.0000 Marital_Status
-0.0010 ± 0.0071 NumMonths
… 3 more …

2.c. Measuring fairness

Of Baseline model

In [89]:
import pandas as pd
import csv
import os
import numpy as np
import sys
from aif360.metrics import *
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, roc_curve, auc
plot_model_performance(mdl_xgb, X_test, y_test)
In [90]:
fair_xg = get_fair_metrics_and_plot(filename, data_orig_test, model_xg)
fair_xg
Computing fairness of the model.
Out[90]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.00000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.693333 0.81746 0.857143 -0.142857 -0.137931 -0.145889 0.058693 0.973333 0.070832

PRE PROCESSING

In [91]:
### Reweighing
from aif360.algorithms.preprocessing import Reweighing

RW_xg = Reweighing(unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)

data_transf_train_xg_rw = RW_xg.fit_transform(data_orig_train)

#train and save model
xg_transf_rw = model_xg.fit(data_transf_train_xg_rw.features,
                     data_transf_train_xg_rw.labels.ravel())

data_transf_test_xg_rw = RW_xg.transform(data_orig_test)
fair_xg_rw = get_fair_metrics_and_plot(filename, data_transf_test_xg_rw, xg_transf_rw, plot=False)
Computing fairness of the model.
In [92]:
fair_xg_rw
Out[92]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.667919 0.799861 0.859069 -0.140931 -0.137931 -0.145889 -0.071317 0.973333 0.070832
In [93]:
from aif360.algorithms.preprocessing import DisparateImpactRemover

DIR_xg = DisparateImpactRemover()
data_transf_train_xg_dir = DIR_xg.fit_transform(data_orig_train)

# Train and save the model
xg_transf_dir = model_xg.fit(data_transf_train_xg_dir.features,data_transf_train_xg_dir.labels.ravel())
In [94]:
fair_dir_xg_dir = get_fair_metrics_and_plot(filename,data_orig_test, xg_transf_dir, plot=False)
fair_dir_xg_dir
Computing fairness of the model.
Out[94]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.713333 0.817021 0.987245 -0.011074 -0.060583 0.022656 0.081949 0.962667 0.115187

INPROCESSING

In [95]:
#!pip install --user --upgrade tensorflow==1.15.0
#2.2.0
#!pip uninstall tensorflow
In [96]:
#!pip install "tensorflow==1.15"
#!pip install --upgrade tensorflow-hub
In [97]:
#%tensorflow_version 1.15
import tensorflow  as tf
#from tensorflow.compat.v1 import variable_scope
print('Using TensorFlow version', tf.__version__)
Using TensorFlow version 1.15.0
In [98]:
#sess = tf.compat.v1.Session()
#import tensorflow as tf

sess = tf.compat.v1.Session()
In [99]:
#import tensorflow as tf
#sess = tf.Session()
tf.compat.v1.reset_default_graph()
In [100]:
from aif360.algorithms.inprocessing.adversarial_debiasing import AdversarialDebiasing
#with tf.variable_scope('debiased_classifier',reuse=tf.AUTO_REUSE):
with tf.compat.v1.Session() as sess:
    with tf.variable_scope('scope1',reuse=tf.AUTO_REUSE) as scope:
        debiased_model_xg_ad = AdversarialDebiasing(privileged_groups = privileged_groups,
                          unprivileged_groups = unprivileged_groups,
                          scope_name=scope,
                          num_epochs=10,
                          debias=True,
                          sess=sess)
#train and save the model
        debiased_model_xg_ad.fit(data_orig_train)
        fair_xg_ad = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_xg_ad, plot=False, model_aif=True)
epoch 0; iter: 0; batch classifier loss: 1.478389; batch adversarial loss: 0.751151
epoch 1; iter: 0; batch classifier loss: 1.065830; batch adversarial loss: 0.763925
epoch 2; iter: 0; batch classifier loss: 1.020910; batch adversarial loss: 0.742397
epoch 3; iter: 0; batch classifier loss: 0.812203; batch adversarial loss: 0.722189
epoch 4; iter: 0; batch classifier loss: 0.862393; batch adversarial loss: 0.747904
epoch 5; iter: 0; batch classifier loss: 0.684354; batch adversarial loss: 0.723152
epoch 6; iter: 0; batch classifier loss: 0.720857; batch adversarial loss: 0.741707
epoch 7; iter: 0; batch classifier loss: 0.675932; batch adversarial loss: 0.715083
epoch 8; iter: 0; batch classifier loss: 0.663213; batch adversarial loss: 0.702580
epoch 9; iter: 0; batch classifier loss: 0.654158; batch adversarial loss: 0.724688
Out[100]:
<aif360.algorithms.inprocessing.adversarial_debiasing.AdversarialDebiasing at 0x29324fb6708>
Computing fairness of the model.
In [101]:
fair_xg_ad
Out[101]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.706667 0.821862 0.863454 -0.131783 -0.161364 -0.112151 0.101883 [0.9566666666666667] 0.079767
In [102]:
from aif360.algorithms.inprocessing import PrejudiceRemover
debiased_model_pr_xg = PrejudiceRemover()

# Train and save the model
debiased_model_pr_xg.fit(data_orig_train)

fair_xg_pr = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_pr_xg, plot=False, model_aif=True)
fair_xg_pr
Out[102]:
<aif360.algorithms.inprocessing.prejudice_remover.PrejudiceRemover at 0x29325cbb488>
Computing fairness of the model.
Out[102]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.00000 0.000000 0.00000 0.000000 1 0.000000
Age 0.696667 0.809224 0.719665 -0.25969 -0.346161 -0.20405 0.228682 [0.894] 0.113402
#
In [103]:
y_pred = debiased_model_pr_xg.predict(data_orig_test)


data_orig_test_pred = data_orig_test.copy(deepcopy=True)
In [104]:
# Prediction with the original RandomForest model
scores = np.zeros_like(data_orig_test.labels)
scores = mdl_xgb.predict_proba(data_orig_test.features)[:,1].reshape(-1,1)
data_orig_test_pred.scores = scores

preds = np.zeros_like(data_orig_test.labels)
preds = mdl_xgb.predict(data_orig_test.features).reshape(-1,1)
data_orig_test_pred.labels = preds

def format_probs(probs1):
    probs1 = np.array(probs1)
    probs0 = np.array(1-probs1)
    return np.concatenate((probs0, probs1), axis=1)

POST PROCESSING

In [105]:
from aif360.algorithms.postprocessing import EqOddsPostprocessing
EOPP_xg = EqOddsPostprocessing(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups,
                             seed=40)
EOPP_xg = EOPP_xg.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_xg_eopp = EOPP_xg.predict(data_orig_test_pred)
fair_xg_eo = fair_metrics(filename, data_orig_test, data_transf_test_pred_xg_eopp, pred_is_dataset=True)
Computing fairness of the model.
In [106]:
fair_xg_eo
Out[106]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.00000
Age 0.616667 0.753747 1.000646 0.000554 0.027815 -0.017561 -0.030454 [0.8093333333333331] 0.17806
In [107]:
from aif360.algorithms.postprocessing import CalibratedEqOddsPostprocessing
cost_constraint = "fnr"
CPP_xg = CalibratedEqOddsPostprocessing(privileged_groups = privileged_groups,
                                     unprivileged_groups = unprivileged_groups,
                                     cost_constraint=cost_constraint,
                                     seed=42)

CPP_xg = CPP_xg.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_xg_cpp = CPP_xg.predict(data_orig_test_pred)
fair_xg_ceo = fair_metrics(filename, data_orig_test, data_transf_test_pred_xg_cpp, pred_is_dataset=True)
Computing fairness of the model.
In [108]:
fair_xg_ceo
Out[108]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.713333 0.817021 0.987245 -0.011074 -0.060583 0.022656 0.081949 [0.9626666666666667] 0.115187
In [109]:
from aif360.algorithms.postprocessing import RejectOptionClassification
ROC_xg = RejectOptionClassification(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups)

ROC_xg = ROC_xg.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_xg_roc = ROC_xg.predict(data_orig_test_pred)
fair_xg_roc = fair_metrics(filename, data_orig_test, data_transf_test_pred_xg_roc, pred_is_dataset=True)
print('SUCCESS: completed 1 model.')
Computing fairness of the model.
SUCCESS: completed 1 model.
In [110]:
fair_xg_roc
Out[110]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.00 1.000000 1.00000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.62 0.686813 1.02381 0.012182 -0.090493 0.081127 0.139535 [0.7879999999999998] 0.371379
In [ ]:
 
In [ ]:
 

3. XGBOOST with out hyper-parameter tuning

In [111]:
from xgboost import XGBClassifier
model_xgb2 = XGBClassifier(seed=40)
In [112]:
mdl_xgb2 = model_xgb2.fit(data_orig_train.features, data_orig_train.labels.ravel())
In [113]:
conf_mat_xg2 = confusion_matrix(data_orig_test.labels.ravel(), model_xgb2.predict(data_orig_test.features))
conf_mat_xg2
from sklearn.metrics import accuracy_score
print(accuracy_score(data_orig_test.labels.ravel(), model_xgb2.predict(data_orig_test.features)))
Out[113]:
array([[ 32,  58],
       [ 36, 174]], dtype=int64)
0.6866666666666666

3.a. Feature importance of model

In [114]:
importances_xg2 = model_xgb2.feature_importances_
indices_xg2 = np.argsort(importances_xg2)
features2 = data_orig_train.feature_names
#https://stackoverflow.com/questions/48377296/get-feature-importance-from-gridsearchcv
In [115]:
importances_xg2
Out[115]:
array([0.02361056, 0.07240672, 0.02394954, 0.04656706, 0.08524894,
       0.02640945, 0.03555013, 0.04276526, 0.03081001, 0.05483867,
       0.0554199 , 0.03113955, 0.04219624, 0.09255903, 0.02998231,
       0.04272536, 0.02681142, 0.02949912, 0.        , 0.08695158,
       0.0307787 , 0.0283696 , 0.06141086], dtype=float32)
In [116]:
importances_xg2[indices_xg2]
Out[116]:
array([0.        , 0.02361056, 0.02394954, 0.02640945, 0.02681142,
       0.0283696 , 0.02949912, 0.02998231, 0.0307787 , 0.03081001,
       0.03113955, 0.03555013, 0.04219624, 0.04272536, 0.04276526,
       0.04656706, 0.05483867, 0.0554199 , 0.06141086, 0.07240672,
       0.08524894, 0.08695158, 0.09255903], dtype=float32)
In [117]:
features2
Out[117]:
['Gender',
 'Age',
 'Marital_Status',
 'NumMonths',
 'Savings_<500',
 'Savings_none',
 'Dependents',
 'Property_rent',
 'Job_management/self-emp/officer/highly qualif emp',
 'Debtors_guarantor',
 'Purpose_CarNew',
 'Purpose_furniture/equip',
 'CreditHistory_none/paid',
 'Purpose_CarUsed',
 'CreditAmount',
 'Collateral_real estate',
 'Debtors_none',
 'Job_unemp/unskilled-non resident',
 'Purpose_others',
 'CreditHistory_other',
 'PayBackPercent',
 'Collateral_unknown/none',
 'Purpose_education']
In [118]:
plt.figure(figsize=(20,30))
plt.title('Feature Importances')
plt.barh(range(len(indices_xg2)), importances_xg2[indices_xg2], color='b', align='center')
plt.yticks(range(len(indices_xg2)), [features2[i] for i in indices_xg2])
plt.xlabel('Relative Importance')
plt.show()
Out[118]:
<Figure size 1440x2160 with 0 Axes>
Out[118]:
Text(0.5, 1.0, 'Feature Importances')
Out[118]:
<BarContainer object of 23 artists>
Out[118]:
([<matplotlib.axis.YTick at 0x29328c52388>,
  <matplotlib.axis.YTick at 0x29328c4d908>,
  <matplotlib.axis.YTick at 0x29325ae73c8>,
  <matplotlib.axis.YTick at 0x29328ca9248>,
  <matplotlib.axis.YTick at 0x29328caaac8>,
  <matplotlib.axis.YTick at 0x29328cae508>,
  <matplotlib.axis.YTick at 0x29328caed48>,
  <matplotlib.axis.YTick at 0x29328cb2608>,
  <matplotlib.axis.YTick at 0x29328cb2dc8>,
  <matplotlib.axis.YTick at 0x29328cb67c8>,
  <matplotlib.axis.YTick at 0x29328cb6f48>,
  <matplotlib.axis.YTick at 0x29328caadc8>,
  <matplotlib.axis.YTick at 0x29328cbb7c8>,
  <matplotlib.axis.YTick at 0x29328cbd148>,
  <matplotlib.axis.YTick at 0x29328cbdd48>,
  <matplotlib.axis.YTick at 0x29328cc2848>,
  <matplotlib.axis.YTick at 0x29328cc5348>,
  <matplotlib.axis.YTick at 0x29328cc5e08>,
  <matplotlib.axis.YTick at 0x29328cc9908>,
  <matplotlib.axis.YTick at 0x29328cc51c8>,
  <matplotlib.axis.YTick at 0x29328cbbfc8>,
  <matplotlib.axis.YTick at 0x29328ccc588>,
  <matplotlib.axis.YTick at 0x29328cd01c8>],
 <a list of 23 Text yticklabel objects>)
Out[118]:
Text(0.5, 0, 'Relative Importance')

3.b. Model Explainability/interpretability

3.b.1 Using SHAP (SHapley Additive exPlanations)

In [119]:
import shap
xg_shap_values_t = shap.KernelExplainer(mdl_xgb2.predict,data_orig_train.features)
WARNING:shap:Using 700 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

Test data interpretation

In [120]:
xgb_explainer2 = shap.KernelExplainer(mdl_xgb2.predict, data_orig_test.features)
xgb_shap_values2 = xgb_explainer2.shap_values(data_orig_test.features,nsamples=10)
#https://towardsdatascience.com/explain-any-models-with-the-shap-values-use-the-kernelexplainer-79de9464897a
WARNING:shap:Using 300 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

In [121]:
xgb_shap_values2
Out[121]:
array([[ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        , ...,  0.035     ,
         0.        ,  0.        ],
       ...,
       [ 0.15666667,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.06333333,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        , -0.61166667,  0.        , ...,  0.        ,
         0.        ,  0.        ]])
In [122]:
shap.initjs()
shap.force_plot(xgb_explainer2.expected_value,xgb_shap_values2[0,:],  data_orig_test.features[0],data_orig_test.feature_names,link='logit')
#https://github.com/slundberg/shap
#https://github.com/slundberg/shap/issues/279
Out[122]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.

The field age is pushing target outcome towards lower value and collateral unknown/none, purpose others are pushing the target towards higher value which resulted in the final probability of occurrance as .73

In [123]:
shap.initjs()
shap.force_plot(xgb_explainer2.expected_value,xgb_shap_values2[1,:],  data_orig_test.features[1],data_orig_test.feature_names,link='logit')
Out[123]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.

Here only credit amount has impact in moving target outcome towards lower value than the base value which is the mean value to target outcome

In [124]:
data_orig_test.feature_names
Out[124]:
['Gender',
 'Age',
 'Marital_Status',
 'NumMonths',
 'Savings_<500',
 'Savings_none',
 'Dependents',
 'Property_rent',
 'Job_management/self-emp/officer/highly qualif emp',
 'Debtors_guarantor',
 'Purpose_CarNew',
 'Purpose_furniture/equip',
 'CreditHistory_none/paid',
 'Purpose_CarUsed',
 'CreditAmount',
 'Collateral_real estate',
 'Debtors_none',
 'Job_unemp/unskilled-non resident',
 'Purpose_others',
 'CreditHistory_other',
 'PayBackPercent',
 'Collateral_unknown/none',
 'Purpose_education']
In [125]:
shap.force_plot(xgb_explainer2.expected_value,
                xgb_shap_values2, data_orig_test.features[:,:],feature_names = data_orig_test.feature_names)
Out[125]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [126]:
p = shap.summary_plot(xgb_shap_values2, data_orig_test.features, feature_names=data_orig_test.feature_names,plot_type="bar") 
display(p)
None

variables with higher impact are CreditAmount,NumMonths,Savings.

In [127]:
shap.plots._waterfall.waterfall_legacy(xgb_explainer2.expected_value, xgb_shap_values2[0,:],feature_names=data_orig_test.feature_names)

Here purpose other and collateral unknown/none are pushing target to higher value and age is pushing it towards lower value.

Interpretation of graph: https://shap.readthedocs.io/en/latest/example_notebooks/overviews/An%20introduction%20to%20explainable%20AI%20with%20Shapley%20values.html

  • f(x)- model output impacted by features; E(f(x))- expected output.

  • One the fundemental properties of Shapley values is that they always sum up to the difference between the game outcome when all players are present and the game outcome when no players are present. For machine learning models this means that SHAP values of all the input features will always sum up to the difference between baseline (expected) model output and the current model output for the prediction being explained.

In [128]:
shap.plots._waterfall.waterfall_legacy(xgb_explainer2.expected_value, xgb_shap_values2[1],feature_names=data_orig_test.feature_names)

3.b.2 Using ELI5

In [129]:
#!pip install eli5
import eli5
from eli5.sklearn import PermutationImportance
In [130]:
perm_xgb2 = PermutationImportance(mdl_xgb2).fit(data_orig_test.features, data_orig_test.labels.ravel())

Feature Importance

In [131]:
perm_imp_3=eli5.show_weights(perm_xgb2,feature_names = data_orig_test.feature_names)
perm_imp_3
plt.show()
Out[131]:
Weight Feature
0.0347 ± 0.0417 CreditAmount
0.0187 ± 0.0341 NumMonths
0.0120 ± 0.0374 Savings_<500
0.0093 ± 0.0115 Job_management/self-emp/officer/highly qualif emp
0.0067 ± 0.0223 Purpose_CarNew
0.0033 ± 0.0119 Property_rent
0.0027 ± 0.0078 Savings_none
0.0020 ± 0.0108 Gender
0.0007 ± 0.0078 Job_unemp/unskilled-non resident
0.0007 ± 0.0050 Dependents
0.0007 ± 0.0088 Debtors_none
0 ± 0.0000 Purpose_others
-0.0007 ± 0.0107 Collateral_unknown/none
-0.0013 ± 0.0033 Debtors_guarantor
-0.0013 ± 0.0241 CreditHistory_other
-0.0020 ± 0.0068 Purpose_education
-0.0033 ± 0.0193 CreditHistory_none/paid
-0.0040 ± 0.0142 Collateral_real estate
-0.0040 ± 0.0281 PayBackPercent
-0.0067 ± 0.0103 Purpose_furniture/equip
… 3 more …

Explaining individual predictions

In [132]:
from eli5 import show_prediction
show_prediction(mdl_xgb2, data_orig_test.features[1], show_feature_values=True,feature_names = data_orig_test.feature_names)
Out[132]:

y=0.0 (probability 0.867, score -1.871) top features

Contribution? Feature Value
+2.091 NumMonths 60.000
+0.394 Collateral_unknown/none 1.000
+0.370 Savings_<500 1.000
+0.320 PayBackPercent 3.000
+0.229 Job_management/self-emp/officer/highly qualif emp 0.000
+0.208 CreditHistory_other 0.000
+0.152 Purpose_CarUsed 0.000
+0.134 Savings_none 0.000
+0.115 Collateral_real estate 0.000
+0.044 CreditAmount 0.362
+0.022 Debtors_guarantor 0.000
-0.006 Purpose_education 0.000
-0.014 Debtors_none 1.000
-0.015 Job_unemp/unskilled-non resident 0.000
-0.068 Dependents 1.000
-0.073 CreditHistory_none/paid 0.000
-0.095 Age 1.000
-0.137 Property_rent 0.000
-0.141 Purpose_furniture/equip 0.000
-0.171 Purpose_CarNew 0.000
-0.200 Gender 1.000
-0.279 Marital_Status 1.000
-1.011 <BIAS> 1.000
In [ ]:
 

3.c. Measuring fairness

Of Baseline model

In [133]:
import pandas as pd
import csv
import os
import numpy as np
import sys
from aif360.metrics import *
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, roc_curve, auc
plot_model_performance(mdl_xgb2, X_test, y_test)
In [134]:
fair_xg2 = get_fair_metrics_and_plot(filename, data_orig_test, mdl_xgb2)
fair_xg2
Computing fairness of the model.
Out[134]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.00000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.686667 0.78733 0.676008 -0.262458 -0.281196 -0.247491 0.133998 0.789333 0.178936

PRE PROCESSING

In [135]:
### Reweighing
from aif360.algorithms.preprocessing import Reweighing

RW_xg2 = Reweighing(unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)

data_transf_train_xg2_rw = RW_xg2.fit_transform(data_orig_train)

#train and save model
xg2_transf_rw = model_xgb2.fit(data_transf_train_xg2_rw.features,
                     data_transf_train_xg2_rw.labels.ravel())

data_transf_test_xg2_rw = RW_xg2.transform(data_orig_test)
fair_xg2_rw = get_fair_metrics_and_plot(filename, data_transf_test_xg2_rw, xg2_transf_rw, plot=False)
Computing fairness of the model.
In [136]:
fair_xg2_rw
Out[136]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.665822 0.768926 0.702008 -0.238862 -0.281196 -0.247491 0.104182 0.789333 0.178936
In [137]:
from aif360.algorithms.preprocessing import DisparateImpactRemover

DIR_xg2 = DisparateImpactRemover()
data_transf_train_xg2_dir = DIR_xg2.fit_transform(data_orig_train)

# Train and save the model
xg2_transf_dir = model_xgb2.fit(data_transf_train_xg2_dir.features,data_transf_train_xg2_dir.labels.ravel())
In [138]:
fair_dir_xg2_dir = get_fair_metrics_and_plot(filename,data_orig_test, xg2_transf_dir, plot=False)
fair_dir_xg2_dir
Computing fairness of the model.
Out[138]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.00 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.71 0.797203 0.756044 -0.184385 -0.264622 -0.129314 0.188815 0.798667 0.185788

INPROCESSING

In [139]:
#!pip install --user --upgrade tensorflow==1.15.0
#2.2.0
#!pip uninstall tensorflow
In [140]:
#!pip install "tensorflow==1.15"
#!pip install --upgrade tensorflow-hub
In [141]:
#%tensorflow_version 1.15
import tensorflow  as tf
#from tensorflow.compat.v1 import variable_scope
print('Using TensorFlow version', tf.__version__)
Using TensorFlow version 1.15.0
In [142]:
#sess = tf.compat.v1.Session()
#import tensorflow as tf

sess = tf.compat.v1.Session()
In [143]:
#import tensorflow as tf
#sess = tf.Session()
tf.compat.v1.reset_default_graph()
In [144]:
from aif360.algorithms.inprocessing.adversarial_debiasing import AdversarialDebiasing
#with tf.variable_scope('debiased_classifier',reuse=tf.AUTO_REUSE):
with tf.compat.v1.Session() as sess:
    with tf.variable_scope('scope1',reuse=tf.AUTO_REUSE) as scope:
        debiased_model_xg2_ad = AdversarialDebiasing(privileged_groups = privileged_groups,
                          unprivileged_groups = unprivileged_groups,
                          scope_name=scope,
                          num_epochs=10,
                          debias=True,
                          sess=sess)
#train and save the model
        debiased_model_xg2_ad.fit(data_orig_train)
        fair_xg2_ad = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_xg2_ad, plot=False, model_aif=True)
epoch 0; iter: 0; batch classifier loss: 0.919759; batch adversarial loss: 0.749025
epoch 1; iter: 0; batch classifier loss: 1.054860; batch adversarial loss: 0.732528
epoch 2; iter: 0; batch classifier loss: 0.792483; batch adversarial loss: 0.760840
epoch 3; iter: 0; batch classifier loss: 0.836376; batch adversarial loss: 0.768606
epoch 4; iter: 0; batch classifier loss: 0.751638; batch adversarial loss: 0.779559
epoch 5; iter: 0; batch classifier loss: 0.755502; batch adversarial loss: 0.797873
epoch 6; iter: 0; batch classifier loss: 0.774266; batch adversarial loss: 0.771641
epoch 7; iter: 0; batch classifier loss: 0.696989; batch adversarial loss: 0.841966
epoch 8; iter: 0; batch classifier loss: 0.833210; batch adversarial loss: 0.832413
epoch 9; iter: 0; batch classifier loss: 0.693729; batch adversarial loss: 0.789277
Out[144]:
<aif360.algorithms.inprocessing.adversarial_debiasing.AdversarialDebiasing at 0x29329870cc8>
Computing fairness of the model.
In [145]:
fair_xg2_ad
Out[145]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.00000 0.000000 1 0.00000
Age 0.703333 0.823062 0.887835 -0.111296 -0.103448 -0.11566 0.042636 [0.976] 0.06703
In [146]:
from aif360.algorithms.inprocessing import PrejudiceRemover
debiased_model_pr_xg2 = PrejudiceRemover()

# Train and save the model
debiased_model_pr_xg2.fit(data_orig_train)

fair_xg2_pr = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_pr_xg2, plot=False, model_aif=True)
fair_xg2_pr
Out[146]:
<aif360.algorithms.inprocessing.prejudice_remover.PrejudiceRemover at 0x293298f2888>
Computing fairness of the model.
Out[146]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.00000 0.000000 0.00000 0.000000 1 0.000000
Age 0.696667 0.809224 0.719665 -0.25969 -0.346161 -0.20405 0.228682 [0.894] 0.113402
#
In [147]:
y_pred = debiased_model_pr_xg2.predict(data_orig_test)


data_orig_test_pred = data_orig_test.copy(deepcopy=True)
In [148]:
# Prediction with the original RandomForest model
scores = np.zeros_like(data_orig_test.labels)
scores = mdl_xgb2.predict_proba(data_orig_test.features)[:,1].reshape(-1,1)
data_orig_test_pred.scores = scores

preds = np.zeros_like(data_orig_test.labels)
preds = mdl_xgb2.predict(data_orig_test.features).reshape(-1,1)
data_orig_test_pred.labels = preds

def format_probs(probs1):
    probs1 = np.array(probs1)
    probs0 = np.array(1-probs1)
    return np.concatenate((probs0, probs1), axis=1)

POST PROCESSING

In [149]:
from aif360.algorithms.postprocessing import EqOddsPostprocessing
EOPP_xg2 = EqOddsPostprocessing(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups,
                             seed=40)
EOPP_xg2 = EOPP_xg2.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_xg2_eopp = EOPP_xg2.predict(data_orig_test_pred)
fair_xg2_eo = fair_metrics(filename, data_orig_test, data_transf_test_pred_xg2_eopp, pred_is_dataset=True)
Computing fairness of the model.
In [150]:
fair_xg2_eo
Out[150]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.576667 0.666667 1.002915 0.001661 -0.021528 0.018207 0.033776 [0.6046666666666662] 0.372967
In [151]:
from aif360.algorithms.postprocessing import CalibratedEqOddsPostprocessing
cost_constraint = "fnr"
CPP_xg2 = CalibratedEqOddsPostprocessing(privileged_groups = privileged_groups,
                                     unprivileged_groups = unprivileged_groups,
                                     cost_constraint=cost_constraint,
                                     seed=42)

CPP_xg2 = CPP_xg2.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_xg2_cpp = CPP_xg2.predict(data_orig_test_pred)
fair_xg2_ceo = fair_metrics(filename, data_orig_test, data_transf_test_pred_xg2_cpp, pred_is_dataset=True)
Computing fairness of the model.
In [152]:
fair_xg2_ceo
Out[152]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.00 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.68 0.804878 0.571429 -0.428571 -0.413793 -0.437666 0.153931 [0.9373333333333334] 0.097923
In [153]:
from aif360.algorithms.postprocessing import RejectOptionClassification
ROC_xg2 = RejectOptionClassification(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups)

ROC_xg2 = ROC_xg2.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_xg2_roc = ROC_xg2.predict(data_orig_test_pred)
fair_xg2_roc = fair_metrics(filename, data_orig_test, data_transf_test_pred_xg2_roc, pred_is_dataset=True)
print('SUCCESS: completed 1 model.')
Computing fairness of the model.
SUCCESS: completed 1 model.
In [154]:
fair_xg2_roc
Out[154]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.00000 0.000000 1 0.00000
Age 0.656667 0.708215 0.941244 -0.028239 -0.090493 0.01769 0.099114 [0.7213333333333329] 0.35984
In [ ]:
 

4. RANDOM FOREST CLASSIFIER MODEL WITH OUT HYPER-PARAMETER TUNING

In [155]:
#Creating the classifier
rf_model2 = RandomForestClassifier(random_state=40)
model_rf2=rf_model2
In [156]:
mdl_rf2 = model_rf2.fit(data_orig_train.features, data_orig_train.labels.ravel())
In [157]:
from sklearn.metrics import confusion_matrix
conf_mat_rf2 = confusion_matrix(data_orig_test.labels.ravel(), model_rf2.predict(data_orig_test.features))
conf_mat_rf2
from sklearn.metrics import accuracy_score
print(accuracy_score(data_orig_test.labels.ravel(), model_rf2.predict(data_orig_test.features)))
Out[157]:
array([[ 30,  60],
       [ 23, 187]], dtype=int64)
0.7233333333333334
In [158]:
unique, counts = np.unique(data_orig_test.labels.ravel(), return_counts=True)
dict(zip(unique, counts))
Out[158]:
{0.0: 90, 1.0: 210}

4.a. Model Explainability/interpretability

4.a.1 Using SHAP (SHapley Additive exPlanations)

In [159]:
import shap
rf_shap_values_t2 = shap.KernelExplainer(mdl_rf2.predict,data_orig_train.features)
WARNING:shap:Using 700 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

Test data interpretation

In [160]:
rf_explainer2 = shap.KernelExplainer(mdl_rf2.predict, data_orig_test.features)
rf_shap_values2 = rf_explainer2.shap_values(data_orig_test.features,nsamples=10)
#https://towardsdatascience.com/explain-any-models-with-the-shap-values-use-the-kernelexplainer-79de9464897a
WARNING:shap:Using 300 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

In [161]:
rf_shap_values2
Out[161]:
array([[ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        , ...,  0.02      ,
         0.        ,  0.        ],
       ...,
       [ 0.        ,  0.        ,  0.055     , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.08833333,  0.        ],
       [ 0.        , -0.51333333,  0.        , ...,  0.        ,
         0.        ,  0.        ]])
In [162]:
rf_explainer2.expected_value
rf_shap_values2
Out[162]:
0.8233333333333334
Out[162]:
array([[ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        , ...,  0.02      ,
         0.        ,  0.        ],
       ...,
       [ 0.        ,  0.        ,  0.055     , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.08833333,  0.        ],
       [ 0.        , -0.51333333,  0.        , ...,  0.        ,
         0.        ,  0.        ]])
In [163]:
shap.initjs()
shap.force_plot(rf_explainer2.expected_value,rf_shap_values2[0,:],  data_orig_test.features[0],data_orig_test.feature_names,link='logit')
#https://github.com/slundberg/shap
#https://github.com/slundberg/shap/issues/279
Out[163]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [164]:
shap.initjs()
shap.force_plot(rf_explainer2.expected_value,rf_shap_values2[1,:], data_orig_test.features[1],data_orig_test.feature_names,link='logit')
Out[164]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [165]:
shap.initjs()
shap.force_plot(rf_explainer2.expected_value,rf_shap_values2[2,:], data_orig_test.features[2],data_orig_test.feature_names,link='logit')
Out[165]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [166]:
data_orig_test.feature_names
Out[166]:
['Gender',
 'Age',
 'Marital_Status',
 'NumMonths',
 'Savings_<500',
 'Savings_none',
 'Dependents',
 'Property_rent',
 'Job_management/self-emp/officer/highly qualif emp',
 'Debtors_guarantor',
 'Purpose_CarNew',
 'Purpose_furniture/equip',
 'CreditHistory_none/paid',
 'Purpose_CarUsed',
 'CreditAmount',
 'Collateral_real estate',
 'Debtors_none',
 'Job_unemp/unskilled-non resident',
 'Purpose_others',
 'CreditHistory_other',
 'PayBackPercent',
 'Collateral_unknown/none',
 'Purpose_education']
In [167]:
shap.force_plot(rf_explainer2.expected_value,
                rf_shap_values2, data_orig_test.features[:,:],feature_names = data_orig_test.feature_names)
Out[167]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [168]:
p = shap.summary_plot(rf_shap_values2, data_orig_test.features, feature_names=data_orig_test.feature_names,plot_type="bar") 
display(p)
None

Variables with higher impact are displayed at the top such as gender,age,nummonths etc

In [169]:
shap.plots._waterfall.waterfall_legacy(rf_explainer2.expected_value, rf_shap_values2[0,:],feature_names=data_orig_test.feature_names)

Interpretation of graph: https://shap.readthedocs.io/en/latest/example_notebooks/overviews/An%20introduction%20to%20explainable%20AI%20with%20Shapley%20values.html

f(x)- model output impacted by features; E(f(x))- expected output.

One the fundemental properties of Shapley values is that they always sum up to the difference between the game outcome when all players are present and the game outcome when no players are present. For machine learning models this means that SHAP values of all the input features will always sum up to the difference between baseline (expected) model output and the current model output for the prediction being explained.

In [170]:
shap.plots._waterfall.waterfall_legacy(rf_explainer2.expected_value, rf_shap_values2[1],feature_names=data_orig_test.feature_names)

4.a.2 Using ELI5

In [171]:
#!pip install eli5
import eli5
from eli5.sklearn import PermutationImportance
In [172]:
perm_rf2 = PermutationImportance(mdl_rf2).fit(data_orig_test.features, data_orig_test.labels.ravel())
In [173]:
data_orig_test.labels[:10,:].ravel()
Out[173]:
array([0., 0., 1., 1., 1., 0., 1., 1., 1., 1.])

Feature Importance

In [174]:
perm_imp_11=eli5.show_weights(perm_rf2,feature_names = data_orig_test.feature_names)
perm_imp_11
plt.show()
Out[174]:
Weight Feature
0.0267 ± 0.0163 Gender
0.0167 ± 0.0042 Job_management/self-emp/officer/highly qualif emp
0.0147 ± 0.0417 CreditAmount
0.0127 ± 0.0154 NumMonths
0.0100 ± 0.0169 Marital_Status
0.0080 ± 0.0116 CreditHistory_other
0.0060 ± 0.0065 Purpose_furniture/equip
0.0047 ± 0.0196 Age
0.0033 ± 0.0042 Debtors_guarantor
0.0027 ± 0.0265 Savings_<500
0.0020 ± 0.0124 Purpose_CarNew
0.0007 ± 0.0027 Job_unemp/unskilled-non resident
0.0000 ± 0.0094 Purpose_CarUsed
0 ± 0.0000 Purpose_others
-0.0007 ± 0.0050 Debtors_none
-0.0020 ± 0.0100 Savings_none
-0.0033 ± 0.0140 CreditHistory_none/paid
-0.0040 ± 0.0165 PayBackPercent
-0.0047 ± 0.0068 Property_rent
-0.0047 ± 0.0033 Dependents
… 3 more …

Explaining individual predictions

In [175]:
show_prediction(mdl_rf2, data_orig_test.features[0], show_feature_values=True,feature_names = data_orig_test.feature_names)
Out[175]:

y=1.0 (probability 0.650) top features

Contribution? Feature Value
+0.703 <BIAS> 1.000
+0.025 Age 1.000
+0.018 Job_management/self-emp/officer/highly qualif emp 0.000
+0.017 Purpose_CarNew 0.000
+0.016 Property_rent 0.000
+0.015 Dependents 1.000
+0.015 CreditAmount 0.043
+0.011 Collateral_real estate 1.000
+0.010 Purpose_education 0.000
+0.010 Purpose_furniture/equip 0.000
+0.010 Gender 1.000
+0.008 Collateral_unknown/none 0.000
+0.004 Job_unemp/unskilled-non resident 0.000
+0.002 Debtors_none 1.000
+0.000 Purpose_others 0.000
-0.010 Purpose_CarUsed 0.000
-0.010 Debtors_guarantor 0.000
-0.011 Marital_Status 0.000
-0.012 CreditHistory_none/paid 0.000
-0.012 Savings_none 0.000
-0.013 PayBackPercent 4.000
-0.037 Savings_<500 1.000
-0.049 NumMonths 24.000
-0.061 CreditHistory_other 0.000
In [176]:
from eli5 import show_prediction
show_prediction(mdl_rf2, data_orig_test.features[1], show_feature_values=True,feature_names = data_orig_test.feature_names)
Out[176]:

y=0.0 (probability 0.640) top features

Contribution? Feature Value
+0.297 <BIAS> 1.000
+0.202 NumMonths 60.000
+0.074 CreditAmount 0.362
+0.067 Collateral_unknown/none 1.000
+0.055 Savings_<500 1.000
+0.038 CreditHistory_other 0.000
+0.033 Savings_none 0.000
+0.023 Purpose_CarUsed 0.000
+0.007 PayBackPercent 3.000
+0.006 Collateral_real estate 0.000
+0.006 Job_management/self-emp/officer/highly qualif emp 0.000
+0.003 Debtors_guarantor 0.000
+0.002 Marital_Status 1.000
-0.000 Job_unemp/unskilled-non resident 0.000
-0.001 Debtors_none 1.000
-0.002 Purpose_others 0.000
-0.004 Purpose_furniture/equip 0.000
-0.008 Purpose_education 0.000
-0.008 Property_rent 0.000
-0.010 Dependents 1.000
-0.011 Purpose_CarNew 0.000
-0.020 Gender 1.000
-0.038 Age 1.000
-0.073 CreditHistory_none/paid 0.000

4.b. Measuring fairness

Of Baseline model

In [177]:
import pandas as pd
import csv
import os
import numpy as np
import sys
from aif360.metrics import *
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, roc_curve, auc
plot_model_performance(mdl_rf2, X_test, y_test)
In [178]:
fair = get_fair_metrics_and_plot(filename, data_orig_test, mdl_rf2)
fair
Computing fairness of the model.
Out[178]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.723333 0.818381 0.691763 -0.265227 -0.273004 -0.256382 0.121262 0.847333 0.130518
In [179]:
type(data_orig_train)
Out[179]:
aif360.datasets.binary_label_dataset.BinaryLabelDataset

PRE PROCESSING

In [180]:
### Reweighing
from aif360.algorithms.preprocessing import Reweighing

RW_rf2 = Reweighing(unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)

data_transf_train_rf2_rw = RW_rf2.fit_transform(data_orig_train)

#train and save model
rf2_transf_rw = model_rf2.fit(data_transf_train_rf2_rw.features,
                     data_transf_train_rf2_rw.labels.ravel())

data_transf_test_rf2_rw = RW_rf2.transform(data_orig_test)
fair_rf2_rw = get_fair_metrics_and_plot(filename, data_transf_test_rf2_rw, rf2_transf_rw, plot=False)
Computing fairness of the model.
In [181]:
fair_rf_rw
Out[181]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.0 0.000000 0.000000 1.000000 0.000000
Age 0.690051 0.814465 0.994353 -0.005597 0.0 -0.025475 -0.159849 0.987333 0.057005
In [182]:
from aif360.algorithms.preprocessing import DisparateImpactRemover

DIR_rf2 = DisparateImpactRemover()
data_transf_train_rf2_dir = DIR_rf2.fit_transform(data_orig_train)

# Train and save the model
rf2_transf_dir = model_rf2.fit(data_transf_train_rf2_dir.features,data_transf_train_rf2_dir.labels.ravel())
In [183]:
fair_dir_rf2_dir = get_fair_metrics_and_plot(filename,data_orig_test, rf2_transf_dir, plot=False)
fair_dir_rf2_dir
Computing fairness of the model.
Out[183]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.00000 0.000000 0.000000 0.000000 1.000000 0.00000
Age 0.703333 0.804396 0.729289 -0.22979 -0.250905 -0.213365 0.125692 0.835333 0.14622
In [184]:
conf_mat_rf2_dir = confusion_matrix(data_orig_test.labels.ravel(), rf2_transf_dir.predict(data_orig_test.features))
conf_mat_rf2_dir
from sklearn.metrics import accuracy_score
print(accuracy_score(data_orig_test.labels.ravel(), rf2_transf_dir.predict(data_orig_test.features)))
Out[184]:
array([[ 28,  62],
       [ 27, 183]], dtype=int64)
0.7033333333333334

INPROCESSING

In [185]:
#!pip install --user --upgrade tensorflow==1.15.0
#2.2.0
#!pip uninstall tensorflow
In [186]:
#!pip install "tensorflow==1.15"
#!pip install --upgrade tensorflow-hub
In [187]:
#%tensorflow_version 1.15
import tensorflow  as tf
#from tensorflow.compat.v1 import variable_scope
print('Using TensorFlow version', tf.__version__)
Using TensorFlow version 1.15.0
In [188]:
#sess = tf.compat.v1.Session()
#import tensorflow as tf

sess = tf.compat.v1.Session()
In [189]:
#import tensorflow as tf
#sess = tf.Session()
tf.compat.v1.reset_default_graph()
In [190]:
from aif360.algorithms.inprocessing.adversarial_debiasing import AdversarialDebiasing
#with tf.variable_scope('debiased_classifier',reuse=tf.AUTO_REUSE):
with tf.compat.v1.Session() as sess:
    with tf.variable_scope('scope1',reuse=tf.AUTO_REUSE) as scope:
        debiased_model_rf2_ad = AdversarialDebiasing(privileged_groups = privileged_groups,
                          unprivileged_groups = unprivileged_groups,
                          scope_name=scope,
                          num_epochs=10,
                          debias=True,
                          sess=sess)
#train and save the model
        debiased_model_rf2_ad.fit(data_orig_train)
        fair_rf2_ad = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_rf2_ad, plot=False, model_aif=True)
epoch 0; iter: 0; batch classifier loss: 0.919759; batch adversarial loss: 0.749025
epoch 1; iter: 0; batch classifier loss: 1.054860; batch adversarial loss: 0.732528
epoch 2; iter: 0; batch classifier loss: 0.792483; batch adversarial loss: 0.760840
epoch 3; iter: 0; batch classifier loss: 0.836376; batch adversarial loss: 0.768606
epoch 4; iter: 0; batch classifier loss: 0.751638; batch adversarial loss: 0.779559
epoch 5; iter: 0; batch classifier loss: 0.755502; batch adversarial loss: 0.797873
epoch 6; iter: 0; batch classifier loss: 0.774266; batch adversarial loss: 0.771641
epoch 7; iter: 0; batch classifier loss: 0.696989; batch adversarial loss: 0.841966
epoch 8; iter: 0; batch classifier loss: 0.833210; batch adversarial loss: 0.832413
epoch 9; iter: 0; batch classifier loss: 0.693729; batch adversarial loss: 0.789277
Out[190]:
<aif360.algorithms.inprocessing.adversarial_debiasing.AdversarialDebiasing at 0x2932d0485c8>
Computing fairness of the model.
In [191]:
fair_rf2_ad
Out[191]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.00000 0.000000 1 0.00000
Age 0.703333 0.823062 0.887835 -0.111296 -0.103448 -0.11566 0.042636 [0.976] 0.06703
In [192]:
from aif360.algorithms.inprocessing import PrejudiceRemover
debiased_model_pr_rf2 = PrejudiceRemover()

# Train and save the model
debiased_model_pr_rf2.fit(data_orig_train)

fair_rf2_pr = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_pr_rf2, plot=False, model_aif=True)
fair_rf2_pr
Out[192]:
<aif360.algorithms.inprocessing.prejudice_remover.PrejudiceRemover at 0x2932c984a88>
Computing fairness of the model.
Out[192]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.00000 0.000000 0.00000 0.000000 1 0.000000
Age 0.696667 0.809224 0.719665 -0.25969 -0.346161 -0.20405 0.228682 [0.894] 0.113402
#
In [193]:
y_pred = debiased_model_pr_rf2.predict(data_orig_test)


data_orig_test_pred = data_orig_test.copy(deepcopy=True)
In [194]:
# Prediction with the original RandomForest model
scores = np.zeros_like(data_orig_test.labels)
scores = mdl_rf2.predict_proba(data_orig_test.features)[:,1].reshape(-1,1)
data_orig_test_pred.scores = scores

preds = np.zeros_like(data_orig_test.labels)
preds = mdl_rf2.predict(data_orig_test.features).reshape(-1,1)
data_orig_test_pred.labels = preds

def format_probs(probs1):
    probs1 = np.array(probs1)
    probs0 = np.array(1-probs1)
    return np.concatenate((probs0, probs1), axis=1)

POST PROCESSING

In [195]:
from aif360.algorithms.postprocessing import EqOddsPostprocessing
EOPP_rf2 = EqOddsPostprocessing(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups,
                             seed=40)
EOPP_rf2 = EOPP_rf2.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_rf2_eopp = EOPP_rf2.predict(data_orig_test_pred)
fair_rf2_eo = fair_metrics(filename, data_orig_test, data_transf_test_pred_rf2_eopp, pred_is_dataset=True)
Computing fairness of the model.
In [196]:
fair_rf2_eo
Out[196]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.613333 0.707071 0.998214 -0.001107 -0.013336 0.009316 0.021041 [0.6573333333333324] 0.314431
In [197]:
from aif360.algorithms.postprocessing import CalibratedEqOddsPostprocessing
cost_constraint = "fnr"
CPP_rf2 = CalibratedEqOddsPostprocessing(privileged_groups = privileged_groups,
                                     unprivileged_groups = unprivileged_groups,
                                     cost_constraint=cost_constraint,
                                     seed=42)

CPP_rf2 = CPP_rf2.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_rf2_cpp = CPP_rf2.predict(data_orig_test_pred)
fair_rf2_ceo = fair_metrics(filename, data_orig_test, data_transf_test_pred_rf2_cpp, pred_is_dataset=True)
Computing fairness of the model.
In [198]:
fair_rf2_ceo
Out[198]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.683333 0.808081 0.642857 -0.357143 -0.344828 -0.364721 0.130122 [0.9433333333333334] 0.091083
In [199]:
from aif360.algorithms.postprocessing import RejectOptionClassification
ROC_rf2 = RejectOptionClassification(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups)

ROC_rf2 = ROC_rf2.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_rf2_roc = ROC_rf2.predict(data_orig_test_pred)
fair_rf2_roc = fair_metrics(filename, data_orig_test, data_transf_test_pred_rf2_roc, pred_is_dataset=True)
print('SUCCESS: completed 1 model.')
Computing fairness of the model.
SUCCESS: completed 1 model.
In [200]:
fair_rf2_roc
Out[200]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.00000 0.000000 0.000000 0.000000 1 0.000000
Age 0.683333 0.748011 0.981151 -0.01052 -0.098876 0.051461 0.130122 [0.7479999999999997] 0.294954

5. KNN

In [201]:
from sklearn import neighbors
n_neighbors = 15
knn = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
In [202]:
knn.fit(data_orig_train.features, data_orig_train.labels.ravel())
Out[202]:
KNeighborsClassifier(n_neighbors=15, weights='distance')
In [203]:
conf_mat_knn = confusion_matrix(data_orig_test.labels.ravel(), knn.predict(data_orig_test.features))
conf_mat_knn
from sklearn.metrics import accuracy_score
print(accuracy_score(data_orig_test.labels.ravel(), knn.predict(data_orig_test.features)))
Out[203]:
array([[ 21,  69],
       [ 15, 195]], dtype=int64)
0.72

5.a. Model Explainability/interpretability

5.a.1 Using SHAP (SHapley Additive exPlanations)

In [204]:
knn_explainer = shap.KernelExplainer(knn.predict, data_orig_test.features)
knn_shap_values = knn_explainer.shap_values(data_orig_test.features,nsamples=10)
WARNING:shap:Using 300 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

In [205]:
#shap.dependence_plot(0, knn_shap_values, data_orig_test.features)
In [206]:
# plot the SHAP values for the 0th observation 
shap.force_plot(knn_explainer.expected_value,knn_shap_values[0,:],  data_orig_test.features[0],data_orig_test.feature_names,link='logit') 
Out[206]:
Visualization omitted, Javascript library not loaded!
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In [207]:
# plot the SHAP values for the 1st observation 
shap.force_plot(knn_explainer.expected_value,knn_shap_values[1,:],  data_orig_test.features[1],data_orig_test.feature_names,link='logit') 
Out[207]:
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In [208]:
shap.force_plot(knn_explainer.expected_value, knn_shap_values,  data_orig_test.feature_names,link='logit')
Out[208]:
Visualization omitted, Javascript library not loaded!
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In [209]:
shap.summary_plot(knn_shap_values, data_orig_test.features,feature_names=data_orig_test.feature_names, plot_type="violin")

Feature Importance

perm_imp_11=eli5.show_weights(knn,feature_names = data_orig_test.feature_names) perm_imp_11 plt.show()

Explaining individual predictions

In [210]:
from eli5 import show_prediction
show_prediction(knn, data_orig_test.features[1], show_feature_values=True,feature_names = data_orig_test.feature_names)
Out[210]:
Error: estimator KNeighborsClassifier(n_neighbors=15, weights='distance') is not supported

5.b. Measuring fairness

Of Baseline model

In [211]:
import pandas as pd
import csv
import os
import numpy as np
import sys
from aif360.metrics import *
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, roc_curve, auc
plot_model_performance(knn, X_test, y_test)
In [212]:
fair = get_fair_metrics_and_plot(filename, data_orig_test, knn)
fair
Computing fairness of the model.
Out[212]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.00 1.000000 1.000000 0.00000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.72 0.822785 0.847291 -0.13732 -0.157173 -0.122043 0.089701 0.900667 0.104695

PRE PROCESSING

In [213]:
### Reweighing
from aif360.algorithms.preprocessing import Reweighing

RW_knn = Reweighing(unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)

data_transf_train_knn = RW_knn.fit_transform(data_orig_train)

# Train and save the model
knn_transf_rw = knn.fit(data_transf_train_knn.features,
                     data_transf_train_knn.labels.ravel())

data_transf_test_knn_rw = RW_knn.transform(data_orig_test)
fair_knn_rw = get_fair_metrics_and_plot(filename, data_transf_test_knn_rw, knn_transf_rw, plot=False)
Computing fairness of the model.
In [214]:
fair_knn_rw
Out[214]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.700454 0.807291 0.868172 -0.117544 -0.157173 -0.122043 -0.001361 0.900667 0.104695
In [215]:
from aif360.algorithms.preprocessing import DisparateImpactRemover

DIR = DisparateImpactRemover()
data_transf_train_knn_dir = DIR.fit_transform(data_orig_train)
# Train and save the model
knn_transf_dir = knn.fit(data_transf_train_knn_dir.features,
                     data_transf_train_knn_dir.labels.ravel())
In [216]:
fair_knn_dir = get_fair_metrics_and_plot(filename, data_orig_test, knn_transf_dir, plot=False)
fair_knn_dir
Computing fairness of the model.
Out[216]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.0000 0.000000 1.000000 0.000000
Age 0.676667 0.782998 0.856456 -0.115725 -0.206706 -0.0564 0.177741 0.905333 0.176667

INPROCESSING

In [217]:
#!pip install tensorflow
import tensorflow  as tf
#from tensorflow.compat.v1 import variable_scope
print('Using TensorFlow version', tf.__version__)
Using TensorFlow version 1.15.0
In [218]:
#sess = tf.compat.v1.Session()
#import tensorflow as tf

sess = tf.compat.v1.Session()
In [219]:
#import tensorflow as tf
#sess = tf.Session()
tf.compat.v1.reset_default_graph()
In [220]:
from aif360.algorithms.inprocessing.adversarial_debiasing import AdversarialDebiasing
#with tf.variable_scope('debiased_classifier',reuse=tf.AUTO_REUSE):
with tf.compat.v1.Session() as sess:
    with tf.variable_scope('scope4',reuse=tf.AUTO_REUSE) as scope:
        debiased_model_knn_ad = AdversarialDebiasing(privileged_groups = privileged_groups,
                          unprivileged_groups = unprivileged_groups,
                          scope_name=scope,
                          num_epochs=10,
                          debias=True,
                          sess=sess)
        debiased_model_knn_ad.fit(data_orig_train)
        fair_knn_ad = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_knn_ad, plot=False, model_aif=True)
epoch 0; iter: 0; batch classifier loss: 2.830437; batch adversarial loss: 0.700618
epoch 1; iter: 0; batch classifier loss: 2.144434; batch adversarial loss: 0.696410
epoch 2; iter: 0; batch classifier loss: 2.049826; batch adversarial loss: 0.697858
epoch 3; iter: 0; batch classifier loss: 1.955477; batch adversarial loss: 0.699754
epoch 4; iter: 0; batch classifier loss: 1.673654; batch adversarial loss: 0.698494
epoch 5; iter: 0; batch classifier loss: 1.383798; batch adversarial loss: 0.702549
epoch 6; iter: 0; batch classifier loss: 1.189859; batch adversarial loss: 0.712478
epoch 7; iter: 0; batch classifier loss: 1.187852; batch adversarial loss: 0.744092
epoch 8; iter: 0; batch classifier loss: 1.127027; batch adversarial loss: 0.756069
epoch 9; iter: 0; batch classifier loss: 0.976769; batch adversarial loss: 0.807975
Out[220]:
<aif360.algorithms.inprocessing.adversarial_debiasing.AdversarialDebiasing at 0x29330691e08>
Computing fairness of the model.
In [221]:
fair_knn_ad
Out[221]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.0000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.323333 0.222222 13.4375 0.771318 0.799962 0.751629 -0.343854 [0.8593333333333332] 0.971324
In [222]:
from aif360.algorithms.inprocessing import PrejudiceRemover
debiased_model_knn_pr = PrejudiceRemover()

# Train and save the model
debiased_model_knn_pr.fit(data_orig_train)

fair_knn_pr = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_knn_pr, plot=False, model_aif=True)
fair_knn_pr
Out[222]:
<aif360.algorithms.inprocessing.prejudice_remover.PrejudiceRemover at 0x293309d4d48>
Computing fairness of the model.
Out[222]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.00000 0.000000 0.00000 0.000000 1 0.000000
Age 0.696667 0.809224 0.719665 -0.25969 -0.346161 -0.20405 0.228682 [0.894] 0.113402
#
In [223]:
y_pred = debiased_model_knn_pr.predict(data_orig_test)

data_orig_test_pred = data_orig_test.copy(deepcopy=True)
In [224]:
# Prediction with the original RandomForest model
scores = np.zeros_like(data_orig_test.labels)
scores = knn.predict_proba(data_orig_test.features)[:,1].reshape(-1,1)
data_orig_test_pred.scores = scores

preds = np.zeros_like(data_orig_test.labels)
preds = knn.predict(data_orig_test.features).reshape(-1,1)
data_orig_test_pred.labels = preds

def format_probs(probs1):
    probs1 = np.array(probs1)
    probs0 = np.array(1-probs1)
    return np.concatenate((probs0, probs1), axis=1)

POST PROCESSING

In [225]:
from aif360.algorithms.postprocessing import EqOddsPostprocessing
EOPP_knn = EqOddsPostprocessing(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups,
                             seed=40)
EOPP_knn = EOPP_knn.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_knn_eop = EOPP_knn.predict(data_orig_test_pred)
fair_knn_eo = fair_metrics(filename, data_orig_test, data_transf_test_pred_knn_eop, pred_is_dataset=True)
Computing fairness of the model.
In [226]:
fair_knn_eo
Out[226]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.00000 1 0.000000
Age 0.496667 0.549254 0.966931 -0.013843 -0.028196 -0.003608 0.02381 [0.6499999999999995] 0.545925
In [227]:
from aif360.algorithms.postprocessing import CalibratedEqOddsPostprocessing
cost_constraint = "fnr"
CPP_knn = CalibratedEqOddsPostprocessing(privileged_groups = privileged_groups,
                                     unprivileged_groups = unprivileged_groups,
                                     cost_constraint=cost_constraint,
                                     seed=40)

CPP_knn = CPP_knn.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_knn_cp = CPP_knn.predict(data_orig_test_pred)
fair_knn_ceo = fair_metrics(filename, data_orig_test, data_transf_test_pred_knn_cp, pred_is_dataset=True)
Computing fairness of the model.
In [228]:
fair_knn_ceo
Out[228]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.676667 0.804829 0.690476 -0.309524 -0.344828 -0.287798 0.177741 [0.9486666666666667] 0.091451
In [229]:
from aif360.algorithms.postprocessing import RejectOptionClassification
ROC_knn = RejectOptionClassification(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups)

ROC_knn = ROC_knn.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_knn_roc = ROC_knn.predict(data_orig_test_pred) 
fair_knn_roc = fair_metrics(filename, data_orig_test, data_transf_test_pred_knn_roc, pred_is_dataset=True)
print('SUCCESS: completed 1 model.')
Computing fairness of the model.
SUCCESS: completed 1 model.
In [230]:
fair_knn_roc
Out[230]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.00 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.67 0.760291 0.982857 -0.011628 -0.147266 0.077216 0.197674 [0.8613333333333334] 0.241253

6. Logistic Regression

In [231]:
from sklearn.linear_model import LogisticRegression

lr = LogisticRegression()
In [232]:
lr.fit(data_orig_train.features, data_orig_train.labels.ravel())
Out[232]:
LogisticRegression()
In [233]:
conf_mat_lr = confusion_matrix(data_orig_test.labels.ravel(), lr.predict(data_orig_test.features))
conf_mat_lr
from sklearn.metrics import accuracy_score
print(accuracy_score(data_orig_test.labels.ravel(), lr.predict(data_orig_test.features)))
Out[233]:
array([[ 22,  68],
       [ 20, 190]], dtype=int64)
0.7066666666666667

6.a. Model Explainability/interpretability

6.a.1 Using SHAP (SHapley Additive exPlanations)

In [234]:
lr_explainer = shap.KernelExplainer(lr.predict, data_orig_test.features)
lr_shap_values = lr_explainer.shap_values(data_orig_test.features,nsamples=10)
WARNING:shap:Using 300 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

In [235]:
# plot the SHAP values for the 0th observation 
shap.force_plot(lr_explainer.expected_value,lr_shap_values[0,:],  data_orig_test.features[0],data_orig_test.feature_names,link='logit') 
Out[235]:
Visualization omitted, Javascript library not loaded!
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In [236]:
# plot the SHAP values for the 1st observation 
shap.force_plot(lr_explainer.expected_value,lr_shap_values[1,:],  data_orig_test.features[1],data_orig_test.feature_names,link='logit') 
Out[236]:
Visualization omitted, Javascript library not loaded!
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In [237]:
shap.force_plot(lr_explainer.expected_value, lr_shap_values,  data_orig_test.feature_names,link='logit')
Out[237]:
Visualization omitted, Javascript library not loaded!
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In [238]:
shap.summary_plot(lr_shap_values, data_orig_test.features,feature_names=data_orig_test.feature_names, plot_type="violin")

Feature Importance

perm_imp_11=eli5.show_weights(knn,feature_names = data_orig_test.feature_names) perm_imp_11 plt.show()

Explaining individual predictions

In [239]:
from eli5 import show_prediction
show_prediction(lr, data_orig_test.features[1], show_feature_values=True,feature_names = data_orig_test.feature_names)
Out[239]:

y=0.0 (probability 0.615, score -0.467) top features

Contribution? Feature Value
+2.376 NumMonths 60.000
+0.887 Savings_<500 1.000
+0.722 PayBackPercent 3.000
+0.256 CreditAmount 0.362
+0.199 Dependents 1.000
+0.168 Collateral_unknown/none 1.000
-0.183 Gender 1.000
-0.250 Debtors_none 1.000
-0.389 Marital_Status 1.000
-0.804 Age 1.000
-2.516 <BIAS> 1.000

6.b. Measuring fairness

Of Baseline model

In [240]:
import pandas as pd
import csv
import os
import numpy as np
import sys
from aif360.metrics import *
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, roc_curve, auc
plot_model_performance(lr, X_test, y_test)
In [241]:
fair_lr = get_fair_metrics_and_plot(filename, data_orig_test, lr)
fair_lr
Computing fairness of the model.
Out[241]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.706667 0.811966 0.544304 -0.418605 -0.449609 -0.396633 0.212625 0.883333 0.122465

PRE PROCESSING

In [242]:
### Reweighing
from aif360.algorithms.preprocessing import Reweighing

RW_lr = Reweighing(unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)

data_transf_train_lr = RW_lr.fit_transform(data_orig_train)

# Train and save the model
lr_transf_rw = lr.fit(data_transf_train_knn.features,
                     data_transf_train_knn.labels.ravel())

data_transf_test_lr_rw = RW_lr.transform(data_orig_test)
fair_lr_rw = get_fair_metrics_and_plot(filename, data_transf_test_lr_rw, lr_transf_rw, plot=False)
Computing fairness of the model.
In [243]:
fair_lr_rw
Out[243]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.675195 0.787887 0.555977 -0.404703 -0.449609 -0.396633 0.181001 0.883333 0.122465
In [244]:
from aif360.algorithms.preprocessing import DisparateImpactRemover

DIR = DisparateImpactRemover()
data_transf_train_lr_dir = DIR.fit_transform(data_orig_train)
# Train and save the model
lr_transf_dir = lr.fit(data_transf_train_lr_dir.features,
                     data_transf_train_lr_dir.labels.ravel())
In [245]:
fair_lr_dir = get_fair_metrics_and_plot(filename, data_orig_test, lr_transf_dir, plot=False)
fair_lr_dir
Computing fairness of the model.
Out[245]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
Age 0.706667 0.810345 0.582512 -0.375415 -0.398552 -0.358117 0.184939 0.865333 0.129116

INPROCESSING

In [246]:
#!pip install tensorflow
import tensorflow  as tf
#from tensorflow.compat.v1 import variable_scope
print('Using TensorFlow version', tf.__version__)
Using TensorFlow version 1.15.0
In [247]:
#sess = tf.compat.v1.Session()
#import tensorflow as tf

sess = tf.compat.v1.Session()
In [248]:
#import tensorflow as tf
#sess = tf.Session()
tf.compat.v1.reset_default_graph()
In [249]:
from aif360.algorithms.inprocessing.adversarial_debiasing import AdversarialDebiasing
#with tf.variable_scope('debiased_classifier',reuse=tf.AUTO_REUSE):
with tf.compat.v1.Session() as sess:
    with tf.variable_scope('scope5',reuse=tf.AUTO_REUSE) as scope:
        debiased_model_lr_ad = AdversarialDebiasing(privileged_groups = privileged_groups,
                          unprivileged_groups = unprivileged_groups,
                          scope_name=scope,
                          num_epochs=10,
                          debias=True,
                          sess=sess)
        debiased_model_lr_ad.fit(data_orig_train)
        fair_lr_ad = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_lr_ad, plot=False, model_aif=True)
epoch 0; iter: 0; batch classifier loss: 1.772720; batch adversarial loss: 0.721402
epoch 1; iter: 0; batch classifier loss: 1.398136; batch adversarial loss: 0.730901
epoch 2; iter: 0; batch classifier loss: 0.959027; batch adversarial loss: 0.678343
epoch 3; iter: 0; batch classifier loss: 0.779785; batch adversarial loss: 0.703189
epoch 4; iter: 0; batch classifier loss: 0.854520; batch adversarial loss: 0.688182
epoch 5; iter: 0; batch classifier loss: 0.739725; batch adversarial loss: 0.700184
epoch 6; iter: 0; batch classifier loss: 0.724943; batch adversarial loss: 0.689031
epoch 7; iter: 0; batch classifier loss: 0.734326; batch adversarial loss: 0.686084
epoch 8; iter: 0; batch classifier loss: 0.653829; batch adversarial loss: 0.734458
epoch 9; iter: 0; batch classifier loss: 0.747527; batch adversarial loss: 0.674294
Out[249]:
<aif360.algorithms.inprocessing.adversarial_debiasing.AdversarialDebiasing at 0x29334b930c8>
Computing fairness of the model.
In [250]:
fair_lr_ad
Out[250]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.00000 0.000000 0.000000 1 0.000000
Age 0.713333 0.828685 1.003415 0.003322 0.01105 -0.000469 -0.001107 [0.9746666666666667] 0.063201
In [251]:
from aif360.algorithms.inprocessing import PrejudiceRemover
debiased_model_lr_pr = PrejudiceRemover()

# Train and save the model
debiased_model_lr_pr.fit(data_orig_train)

fair_lr_pr = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_lr_pr, plot=False, model_aif=True)
fair_lr_pr
Out[251]:
<aif360.algorithms.inprocessing.prejudice_remover.PrejudiceRemover at 0x29334a1b448>
Computing fairness of the model.
Out[251]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.00000 0.000000 0.00000 0.000000 1 0.000000
Age 0.696667 0.809224 0.719665 -0.25969 -0.346161 -0.20405 0.228682 [0.894] 0.113402
#
In [252]:
y_pred = debiased_model_lr_pr.predict(data_orig_test)

data_orig_test_pred = data_orig_test.copy(deepcopy=True)
In [253]:
# Prediction with the original RandomForest model
scores = np.zeros_like(data_orig_test.labels)
scores = lr.predict_proba(data_orig_test.features)[:,1].reshape(-1,1)
data_orig_test_pred.scores = scores

preds = np.zeros_like(data_orig_test.labels)
preds = lr.predict(data_orig_test.features).reshape(-1,1)
data_orig_test_pred.labels = preds

def format_probs(probs1):
    probs1 = np.array(probs1)
    probs0 = np.array(1-probs1)
    return np.concatenate((probs0, probs1), axis=1)

POST PROCESSING

In [254]:
from aif360.algorithms.postprocessing import EqOddsPostprocessing
EOPP_lr = EqOddsPostprocessing(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups,
                             seed=40)
EOPP_lr = EOPP_lr.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_lr_eop = EOPP_lr.predict(data_orig_test_pred)
fair_lr_eo = fair_metrics(filename, data_orig_test, data_transf_test_pred_lr_eop, pred_is_dataset=True)
Computing fairness of the model.
In [255]:
fair_lr_eo
Out[255]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.00000 0.000000 1 0.000000
Age 0.533333 0.619565 0.993697 -0.003322 0.010288 -0.01084 -0.016611 [0.634666666666666] 0.436309
In [256]:
from aif360.algorithms.postprocessing import CalibratedEqOddsPostprocessing
cost_constraint = "fnr"
CPP_lr = CalibratedEqOddsPostprocessing(privileged_groups = privileged_groups,
                                     unprivileged_groups = unprivileged_groups,
                                     cost_constraint=cost_constraint,
                                     seed=40)

CPP_lr = CPP_lr.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_lr_cp = CPP_lr.predict(data_orig_test_pred)
fair_lr_ceo = fair_metrics(filename, data_orig_test, data_transf_test_pred_lr_cp, pred_is_dataset=True)
Computing fairness of the model.
In [257]:
fair_lr_ceo
Out[257]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.00 1.000000 1.00000 0.00000 0.000000 0.000000 0.000000 1 0.000000
Age 0.68 0.804082 0.52381 -0.47619 -0.448276 -0.493369 0.153931 [0.932] 0.101259
In [258]:
from aif360.algorithms.postprocessing import RejectOptionClassification
ROC_lr = RejectOptionClassification(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups)

ROC_lr = ROC_lr.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_lr_roc = ROC_lr.predict(data_orig_test_pred) 
fair_lr_roc = fair_metrics(filename, data_orig_test, data_transf_test_pred_lr_roc, pred_is_dataset=True)
print('SUCCESS: completed 1 model.')
Computing fairness of the model.
SUCCESS: completed 1 model.
In [259]:
fair_lr_roc
Out[259]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.00 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.67 0.722689 0.967379 -0.016058 -0.072585 0.026644 0.086932 [0.7599999999999998] 0.341011
In [ ]:
 

7. SVM

In [260]:
from sklearn.svm import SVC
#gs = grid_search_cv.best_estimator_
svm = SVC(C=0.85, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',
    max_iter=-1, random_state=42, shrinking=True, tol=0.001, probability=True,
    verbose=False)
svm.fit(data_orig_train.features, data_orig_train.labels.ravel())
Out[260]:
SVC(C=0.85, kernel='linear', probability=True, random_state=42)
In [261]:
conf_mat_svm = confusion_matrix(data_orig_test.labels.ravel(), svm.predict(data_orig_test.features))
conf_mat_svm
from sklearn.metrics import accuracy_score
print(accuracy_score(data_orig_test.labels.ravel(), svm.predict(data_orig_test.features)))
Out[261]:
array([[ 16,  74],
       [ 12, 198]], dtype=int64)
0.7133333333333334

7.a. Model Explainability/interpretability

7.a.1 Using SHAP (SHapley Additive exPlanations)

In [262]:
svm_explainer = shap.KernelExplainer(svm.predict, data_orig_test.features)
svm_shap_values = svm_explainer.shap_values(data_orig_test.features,nsamples=10)
WARNING:shap:Using 300 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.

In [263]:
# plot the SHAP values for the 0th observation 
shap.force_plot(svm_explainer.expected_value,svm_shap_values[0,:],  data_orig_test.features[0],data_orig_test.feature_names,link='logit') 
Out[263]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [264]:
# plot the SHAP values for the 1st observation 
shap.force_plot(svm_explainer.expected_value,svm_shap_values[1,:],  data_orig_test.features[1],data_orig_test.feature_names,link='logit') 
Out[264]:
Visualization omitted, Javascript library not loaded!
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In [265]:
shap.force_plot(svm_explainer.expected_value, svm_shap_values,  data_orig_test.feature_names,link='logit')
Out[265]:
Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
In [266]:
shap.summary_plot(svm_shap_values, data_orig_test.features,feature_names=data_orig_test.feature_names, plot_type="violin")

Feature Importance

perm_imp_11=eli5.show_weights(knn,feature_names = data_orig_test.feature_names) perm_imp_11 plt.show()

Explaining individual predictions

In [267]:
from eli5 import show_prediction
show_prediction(svm, data_orig_test.features[1], show_feature_values=True,feature_names = data_orig_test.feature_names)
Out[267]:

y=0.0 (probability 0.572, score -0.390) top features

Contribution? Feature Value
+1.970 NumMonths 60.000
+0.618 Savings_<500 1.000
+0.531 PayBackPercent 3.000
+0.389 CreditAmount 0.362
+0.136 Dependents 1.000
+0.096 Collateral_unknown/none 1.000
-0.151 Debtors_none 1.000
-0.187 Marital_Status 1.000
-0.199 Gender 1.000
-0.531 Age 1.000
-2.283 <BIAS> 1.000

7.b. Measuring fairness

Of Baseline model

In [268]:
import pandas as pd
import csv
import os
import numpy as np
import sys
from aif360.metrics import *
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, roc_curve, auc
plot_model_performance(svm, X_test, y_test)
In [269]:
fair_svm = get_fair_metrics_and_plot(filename, data_orig_test, svm)
fair_svm
Computing fairness of the model.
Out[269]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.00000 0.00000 0.000000 0.00000 1.00 0.000000
Age 0.713333 0.821577 0.704918 -0.27907 -0.29377 -0.267764 0.13732 0.91 0.095524

PRE PROCESSING

In [270]:
### Reweighing
from aif360.algorithms.preprocessing import Reweighing

RW_svm = Reweighing(unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)

data_transf_train_svm = RW_svm.fit_transform(data_orig_train)

# Train and save the model
svm_transf_rw = svm.fit(data_transf_train_knn.features,
                     data_transf_train_knn.labels.ravel())

data_transf_test_svm_rw = RW_svm.transform(data_orig_test)
fair_svm_rw = get_fair_metrics_and_plot(filename, data_transf_test_svm_rw, svm_transf_rw, plot=False)
Computing fairness of the model.
In [271]:
fair_svm_rw
Out[271]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.00000 0.000000 0.000000 1.00 0.000000
Age 0.685892 0.801315 0.718673 -0.264487 -0.29377 -0.267764 0.063171 0.91 0.095524
In [272]:
from aif360.algorithms.preprocessing import DisparateImpactRemover

DIR = DisparateImpactRemover()
data_transf_train_svm_dir = DIR.fit_transform(data_orig_train)
# Train and save the model
svm_transf_dir = svm.fit(data_transf_train_svm_dir.features,
                     data_transf_train_svm_dir.labels.ravel())
In [273]:
fair_svm_dir = get_fair_metrics_and_plot(filename, data_orig_test, svm_transf_dir, plot=False)
fair_svm_dir
Computing fairness of the model.
Out[273]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.00 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000 0.000000
Age 0.71 0.816068 0.731915 -0.244186 -0.266146 -0.227978 0.133444 0.884 0.112217

INPROCESSING

In [274]:
#!pip install tensorflow
import tensorflow  as tf
#from tensorflow.compat.v1 import variable_scope
print('Using TensorFlow version', tf.__version__)
Using TensorFlow version 1.15.0
In [275]:
#sess = tf.compat.v1.Session()
#import tensorflow as tf

sess = tf.compat.v1.Session()
In [276]:
#import tensorflow as tf
#sess = tf.Session()
tf.compat.v1.reset_default_graph()
In [277]:
from aif360.algorithms.inprocessing.adversarial_debiasing import AdversarialDebiasing
#with tf.variable_scope('debiased_classifier',reuse=tf.AUTO_REUSE):
with tf.compat.v1.Session() as sess:
    with tf.variable_scope('scope6',reuse=tf.AUTO_REUSE) as scope:
        debiased_model_svm_ad = AdversarialDebiasing(privileged_groups = privileged_groups,
                          unprivileged_groups = unprivileged_groups,
                          scope_name=scope,
                          num_epochs=10,
                          debias=True,
                          sess=sess)
        debiased_model_svm_ad.fit(data_orig_train)
        fair_svm_ad = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_svm_ad, plot=False, model_aif=True)
epoch 0; iter: 0; batch classifier loss: 1.772720; batch adversarial loss: 0.721402
epoch 1; iter: 0; batch classifier loss: 1.398136; batch adversarial loss: 0.730901
epoch 2; iter: 0; batch classifier loss: 0.959027; batch adversarial loss: 0.678343
epoch 3; iter: 0; batch classifier loss: 0.779785; batch adversarial loss: 0.703189
epoch 4; iter: 0; batch classifier loss: 0.854520; batch adversarial loss: 0.688182
epoch 5; iter: 0; batch classifier loss: 0.739725; batch adversarial loss: 0.700184
epoch 6; iter: 0; batch classifier loss: 0.724943; batch adversarial loss: 0.689031
epoch 7; iter: 0; batch classifier loss: 0.734326; batch adversarial loss: 0.686084
epoch 8; iter: 0; batch classifier loss: 0.653829; batch adversarial loss: 0.734458
epoch 9; iter: 0; batch classifier loss: 0.747527; batch adversarial loss: 0.674294
Out[277]:
<aif360.algorithms.inprocessing.adversarial_debiasing.AdversarialDebiasing at 0x29337719f08>
Computing fairness of the model.
In [278]:
fair_svm_ad
Out[278]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.00000 0.000000 0.000000 1 0.000000
Age 0.713333 0.828685 1.003415 0.003322 0.01105 -0.000469 -0.001107 [0.9746666666666667] 0.063201
In [279]:
from aif360.algorithms.inprocessing import PrejudiceRemover
debiased_model_svm_pr = PrejudiceRemover()

# Train and save the model
debiased_model_svm_pr.fit(data_orig_train)

fair_svm_pr = get_fair_metrics_and_plot(filename, data_orig_test, debiased_model_svm_pr, plot=False, model_aif=True)
fair_svm_pr
Out[279]:
<aif360.algorithms.inprocessing.prejudice_remover.PrejudiceRemover at 0x29337a2b588>
Computing fairness of the model.
Out[279]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.00000 0.000000 0.00000 0.000000 1 0.000000
Age 0.696667 0.809224 0.719665 -0.25969 -0.346161 -0.20405 0.228682 [0.894] 0.113402
#
In [280]:
y_pred = debiased_model_svm_pr.predict(data_orig_test)

data_orig_test_pred = data_orig_test.copy(deepcopy=True)
In [281]:
# Prediction with the original RandomForest model
scores = np.zeros_like(data_orig_test.labels)
scores = svm.predict_proba(data_orig_test.features)[:,1].reshape(-1,1)
data_orig_test_pred.scores = scores

preds = np.zeros_like(data_orig_test.labels)
preds = svm.predict(data_orig_test.features).reshape(-1,1)
data_orig_test_pred.labels = preds

def format_probs(probs1):
    probs1 = np.array(probs1)
    probs0 = np.array(1-probs1)
    return np.concatenate((probs0, probs1), axis=1)

POST PROCESSING

In [282]:
from aif360.algorithms.postprocessing import EqOddsPostprocessing
EOPP_svm = EqOddsPostprocessing(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups,
                             seed=40)
EOPP_svm = EOPP_svm.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_svm_eop = EOPP_svm.predict(data_orig_test_pred)
fair_svm_eo = fair_metrics(filename, data_orig_test, data_transf_test_pred_svm_eop, pred_is_dataset=True)
Computing fairness of the model.
In [283]:
fair_svm_eo
Out[283]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.00000 0.000000 0.000000 0.000000 0.000000 1 0.00000
Age 0.643333 0.739659 0.99422 -0.003876 -0.039627 0.021645 0.055925 [0.6519999999999995] 0.26389
In [284]:
from aif360.algorithms.postprocessing import CalibratedEqOddsPostprocessing
cost_constraint = "fnr"
CPP_svm = CalibratedEqOddsPostprocessing(privileged_groups = privileged_groups,
                                     unprivileged_groups = unprivileged_groups,
                                     cost_constraint=cost_constraint,
                                     seed=40)

CPP_svm = CPP_svm.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_svm_cp = CPP_svm.predict(data_orig_test_pred)
fair_svm_ceo = fair_metrics(filename, data_orig_test, data_transf_test_pred_svm_cp, pred_is_dataset=True)
Computing fairness of the model.
In [285]:
fair_svm_ceo
Out[285]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.693333 0.815261 0.714286 -0.285714 -0.241379 -0.312997 0.058693 [0.9440000000000001] 0.080621
In [286]:
from aif360.algorithms.postprocessing import RejectOptionClassification
ROC_svm = RejectOptionClassification(privileged_groups = privileged_groups,
                             unprivileged_groups = unprivileged_groups)

ROC_svm = ROC_svm.fit(data_orig_test, data_orig_test_pred)
data_transf_test_pred_svm_roc = ROC_svm.predict(data_orig_test_pred) 
fair_svm_roc = fair_metrics(filename, data_orig_test, data_transf_test_pred_svm_roc, pred_is_dataset=True)
print('SUCCESS: completed 1 model.')
Computing fairness of the model.
SUCCESS: completed 1 model.
In [287]:
fair_svm_roc
Out[287]:
Accuracy F1 DI SPD EOD AOD ERD CNT TI
objective 1.000000 1.00 1.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000
Age 0.693333 0.75 1.046561 0.024363 -0.122309 0.123661 0.197121 [0.7626666666666664] 0.302152
In [ ]:
 

Deploy

In [329]:
import pickle
pickle.dump(rf_transf_dir,open('dir_age_debiased.pkl','wb'))
dir_model_age=pickle.load(open('dir_age_debiased.pkl','rb'))
In [324]:
data_orig_test.features[0]
Out[324]:
array([ 1.        ,  1.        ,  0.        , 24.        ,  1.        ,
        0.        ,  1.        ,  0.        ,  0.        ,  0.        ,
        0.        ,  0.        ,  0.        ,  0.        ,  0.04258831,
        1.        ,  1.        ,  0.        ,  0.        ,  0.        ,
        4.        ,  0.        ,  0.        ])
In [325]:
data_orig_test.labels[0].ravel()
Out[325]:
array([0.])
In [331]:
pred=rf_transf_dir.predict([[ "1"        ,  "1"        ,  "0"        , "24"        ,  "1"        ,    "0"        ,  "1"        ,  "0"        ,  "0"        ,  "0"        ,        "0"        ,  "0"        ,  "0"        ,  "0"        ,  "0.04258831",   "1"        ,  "1"        ,  "0"        ,  "0"        ,  "0"        ,        "4"        ,  "0"        ,  "0"    ]] )
In [332]:
pred[0]
Out[332]:
1.0
In [336]:
German_df['Property_rent'].value_counts()
Out[336]:
0    821
1    179
Name: Property_rent, dtype: int64
In [ ]: